<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p>This course is geared for attendees with Intermediate IT skills who wish to learn Computer Vision with tensor flow 2</p>Overview<p>This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course.  Working in a hands-on learning environment, led by our Computer Vision expert instructor, students will learn about and explore how to<br />Build, train, and serve your own deep neural networks with TensorFlow 2 and Keras<br />Apply modern solutions to a wide range of applications such as object detection and video analysis<br />Run your models on mobile devices and web pages and improve their performance.<br />Create your own neural networks from scratch<br />Classify images with modern architectures including Inception and ResNet<br />Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net<br />Tackle problems faced when developing self-driving cars and facial emotion recognition systems<br />Boost your application's performance with transfer learning, GANs, and domain adaptation<br />Use recurrent neural networks (RNNs) for video analysis<br />Optimize and deploy your networks on mobile devices and in the browser</p><p>Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. Hands-On Computervision with TensorFlow 2 is a hands-on course that thoroughly explores TensorFlow 2, the brand-new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks.<br />This course begins with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the course demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative adversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts.</p></div><div id="course-pills-2"><h4>Computer Vision and Neural Networks </h4><ul><li>Computer Vision and Neural Networks </li><li>Technical requirements </li><li>Computer vision in the wild </li><li>A brief history of computer vision </li><li>Getting started with neural networks </li></ul><h4>TensorFlow Basics and Training a Model </h4><ul><li>TensorFlow Basics and Training a Model </li><li>Technical requirements </li><li>Getting started with TensorFlow 2 and Keras </li><li>TensorFlow 2 and Keras in detail </li><li>The TensorFlow ecosystem </li></ul><h4>Modern Neural Networks </h4><ul><li>Modern Neural Networks </li><li>Technical requirements </li><li>Discovering convolutional neural networks </li><li>Refining the training process </li></ul><h4>Influential Classification Tools </h4><ul><li>Influential Classification Tools </li><li>Technical requirements </li><li>Understanding advanced CNN architectures </li><li>Leveraging transfer learning </li></ul><h4>Object Detection Models </h4><ul><li>Object Detection Models </li><li>Technical requirements </li><li>Introducing object detection </li><li>A fast object detection algorithm ? YOLO </li><li>Faster R-CNN ? a powerful object detection model </li></ul><h4>Enhancing and Segmenting Images </h4><ul><li>Enhancing and Segmenting Images </li><li>Technical requirements </li><li>Transforming images with encoders-decoders </li><li>Understanding semantic segmentation </li></ul><h4>Training on Complex and Scarce Datasets </h4><ul><li>Training on Complex and Scarce Datasets </li><li>Technical requirements </li><li>Efficient data serving </li><li>How to deal with data scarcity </li></ul><h4>Video and Recurrent Neural Networks </h4><ul><li>Video and Recurrent Neural Networks </li><li>Technical requirements </li><li>Introducing RNNs </li><li>Classifying videos </li></ul><h4>Optimizing Models and Deploying on Mobile Devices </h4><ul><li>Optimizing Models and Deploying on Mobile Devices </li><li>Technical requirements </li><li>Optimizing computational and disk footprints </li><li>On-device machine learning </li><li>Example app ? recognizing facial expressions </li></ul></div>

Duration
4 Days
24 CPD hours
This course is intended for
This course is geared for attendees with Intermediate IT skills who wish to learn Computer Vision with tensor flow 2
Overview
This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Working in a hands-on learning environment, led by our Computer Vision expert instructor, students will learn about and explore how to
Build, train, and serve your own deep neural networks with TensorFlow 2 and Keras
Apply modern solutions to a wide range of applications such as object detection and video analysis
Run your models on mobile devices and web pages and improve their performance.
Create your own neural networks from scratch
Classify images with modern architectures including Inception and ResNet
Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net
Tackle problems faced when developing self-driving cars and facial emotion recognition systems
Boost your application's performance with transfer learning, GANs, and domain adaptation
Use recurrent neural networks (RNNs) for video analysis
Optimize and deploy your networks on mobile devices and in the browser
Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. Hands-On Computervision with TensorFlow 2 is a hands-on course that thoroughly explores TensorFlow 2, the brand-new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks.
This course begins with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the course demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative adversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts.
Computer Vision and Neural Networks
Computer Vision and Neural Networks
Technical requirements
Computer vision in the wild
A brief history of computer vision
Getting started with neural networks
TensorFlow Basics and Training a Model
TensorFlow Basics and Training a Model
Technical requirements
Getting started with TensorFlow 2 and Keras
TensorFlow 2 and Keras in detail
The TensorFlow ecosystem
Modern Neural Networks
Modern Neural Networks
Technical requirements
Discovering convolutional neural networks
Refining the training process
Influential Classification Tools
Influential Classification Tools
Technical requirements
Understanding advanced CNN architectures
Leveraging transfer learning
Object Detection Models
Object Detection Models
Technical requirements
Introducing object detection
A fast object detection algorithm ? YOLO
Faster R-CNN ? a powerful object detection model
Enhancing and Segmenting Images
Enhancing and Segmenting Images
Technical requirements
Transforming images with encoders-decoders
Understanding semantic segmentation
Training on Complex and Scarce Datasets
Training on Complex and Scarce Datasets
Technical requirements
Efficient data serving
How to deal with data scarcity
Video and Recurrent Neural Networks
Video and Recurrent Neural Networks
Technical requirements
Introducing RNNs
Classifying videos
Optimizing Models and Deploying on Mobile Devices
Optimizing Models and Deploying on Mobile Devices
Technical requirements
Optimizing computational and disk footprints
On-device machine learning
Example app ? recognizing facial expressions

Nexus Human

Computing Skills

Computer Vision

TensorFlow

Trainers Course

Deep Learning

Hands-On Computervision with TensorFlow 2 (TTML6900)

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>This course is geared for attendees with Intermediate IT skills who wish to learn Computer Vision with tensor flow 2</p>Overview<p>This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Working in a hands-on learning environment, led by our Computer Vision expert instructor, students will learn about and explore how to<br />Build, train, and serve your own deep neural networks with TensorFlow 2 and Keras<br />Apply modern solutions to a wide range of applications such as object detection and video analysis<br />Run your models on mobile devices and web pages and improve their performance.<br />Create your own neural networks from scratch<br />Classify images with modern architectures including Inception and ResNet<br />Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net<br />Tackle problems faced when developing self-driving cars and facial emotion recognition systems<br />Boost your application's performance with transfer learning, GANs, and domain adaptation<br />Use recurrent neural networks (RNNs) for video analysis<br />Optimize and deploy your networks on mobile devices and in the browser</p><p>Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. Hands-On Computervision with TensorFlow 2 is a hands-on course that thoroughly explores TensorFlow 2, the brandnew version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. This course begins with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the course demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative dversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts</p></div><div id="course-pills-2"><h4>Computer Vision and Neural Networks</h4><ul><li>Computer Vision and Neural Networks </li><li>Technical requirements </li><li>Computer vision in the wild </li><li>A brief history of computer vision </li><li>Getting started with neural networks </li></ul><h4>TensorFlow Basics and Training a Model</h4><ul><li>TensorFlow Basics and Training a Model </li><li>Technical requirements </li><li>Getting started with TensorFlow 2 and Keras </li><li>TensorFlow 2 and Keras in detail </li><li>The TensorFlow ecosystem</li></ul><h4>Modern Neural Networks</h4><ul><li>Modern Neural Networks </li><li>Technical requirements </li><li>Discovering convolutional neural networks </li><li>Refining the training process </li></ul><h4>Influential Classification Tools</h4><ul><li>Influential Classification Tools </li><li>Technical requirements </li><li>Understanding advanced CNN architectures </li><li>Leveraging transfer learning </li></ul><h4>Object Detection Models</h4><ul><li>Object Detection Models </li><li>Technical requirements </li><li>Introducing object detection </li><li>A fast object detection algorithm YOLO </li><li>Faster R-CNN ? a powerful object detection model</li></ul><h4>Enhancing and Segmenting Images</h4><ul><li>Enhancing and Segmenting Images </li><li>Technical requirements </li><li>Transforming images with encoders-decoders </li><li>Understanding semantic segmentation </li></ul><h4>Training on Complex and Scarce Datasets</h4><ul><li>Training on Complex and Scarce Datasets </li><li>Technical requirements </li><li>Efficient data serving </li><li>How to deal with data scarcity</li></ul><h4>Video and Recurrent Neural Networks</h4><ul><li>Video and Recurrent Neural Networks </li><li>Technical requirements </li><li>Introducing RNNs </li><li>Classifying videos Optimizing Models and Deploying on Mobile Devices </li><li>Optimizing Models and Deploying on Mobile Devices Technical requirements </li><li>Optimizing computational and disk footprints </li><li>On-device machine learning </li><li>Example app ? recognizing facial expressions</li></ul></div>

Duration
3 Days
18 CPD hours
This course is intended for
This course is geared for attendees with Intermediate IT skills who wish to learn Computer Vision with tensor flow 2
Overview
This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Working in a hands-on learning environment, led by our Computer Vision expert instructor, students will learn about and explore how to
Build, train, and serve your own deep neural networks with TensorFlow 2 and Keras
Apply modern solutions to a wide range of applications such as object detection and video analysis
Run your models on mobile devices and web pages and improve their performance.
Create your own neural networks from scratch
Classify images with modern architectures including Inception and ResNet
Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net
Tackle problems faced when developing self-driving cars and facial emotion recognition systems
Boost your application's performance with transfer learning, GANs, and domain adaptation
Use recurrent neural networks (RNNs) for video analysis
Optimize and deploy your networks on mobile devices and in the browser
Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. Hands-On Computervision with TensorFlow 2 is a hands-on course that thoroughly explores TensorFlow 2, the brandnew version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. This course begins with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the course demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative dversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts
Computer Vision and Neural Networks
Computer Vision and Neural Networks
Technical requirements
Computer vision in the wild
A brief history of computer vision
Getting started with neural networks
TensorFlow Basics and Training a Model
TensorFlow Basics and Training a Model
Technical requirements
Getting started with TensorFlow 2 and Keras
TensorFlow 2 and Keras in detail
The TensorFlow ecosystem
Modern Neural Networks
Modern Neural Networks
Technical requirements
Discovering convolutional neural networks
Refining the training process
Influential Classification Tools
Influential Classification Tools
Technical requirements
Understanding advanced CNN architectures
Leveraging transfer learning
Object Detection Models
Object Detection Models
Technical requirements
Introducing object detection
A fast object detection algorithm YOLO
Faster R-CNN ? a powerful object detection model
Enhancing and Segmenting Images
Enhancing and Segmenting Images
Technical requirements
Transforming images with encoders-decoders
Understanding semantic segmentation
Training on Complex and Scarce Datasets
Training on Complex and Scarce Datasets
Technical requirements
Efficient data serving
How to deal with data scarcity
Video and Recurrent Neural Networks
Video and Recurrent Neural Networks
Technical requirements
Introducing RNNs
Classifying videos Optimizing Models and Deploying on Mobile Devices
Optimizing Models and Deploying on Mobile Devices Technical requirements
Optimizing computational and disk footprints
On-device machine learning
Example app ? recognizing facial expressions

Learning To Learn

Deep Learning with Vision Systems (TTAI3040)

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p></p><p>This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.</p><p></p>Overview<p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.</p><p></p><p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.</p><p></p>Prerequisites<p></p><li>Creating cloud resources in Microsoft Azure.</li>  <li>Using Python to explore and visualize data.</li>  <li>Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.</li>  <li>Working with containers</li>  <li>AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience.</li><p></p></div><div id="course-pills-2"><h3>1 - Design a data ingestion strategy for machine learning projects</h3> <ul> <li>Identify your data source and format</li> <li>Choose how to serve data to machine learning workflows</li> <li>Design a data ingestion solution</li> </ul> <h3>2 - Design a machine learning model training solution</h3> <ul> <li>Identify machine learning tasks</li> <li>Choose a service to train a machine learning model</li> <li>Decide between compute options</li> </ul> <h3>3 - Design a model deployment solution</h3> <ul> <li>Understand how model will be consumed</li> <li>Decide on real-time or batch deployment</li> </ul> <h3>4 - Design a machine learning operations solution</h3> <ul> <li>Explore an MLOps architecture</li> <li>Design for monitoring</li> <li>Design for retraining</li> </ul> <h3>5 - Explore Azure Machine Learning workspace resources and assets</h3> <ul> <li>Create an Azure Machine Learning workspace</li> <li>Identify Azure Machine Learning resources</li> <li>Identify Azure Machine Learning assets</li> <li>Train models in the workspace</li> </ul> <h3>6 - Explore developer tools for workspace interaction</h3> <ul> <li>Explore the studio</li> <li>Explore the Python SDK</li> <li>Explore the CLI</li> </ul> <h3>7 - Make data available in Azure Machine Learning</h3> <ul> <li>Understand URIs</li> <li>Create a datastore</li> <li>Create a data asset</li> </ul> <h3>8 - Work with compute targets in Azure Machine Learning</h3> <ul> <li>Choose the appropriate compute target</li> <li>Create and use a compute instance</li> <li>Create and use a compute cluster</li> </ul> <h3>9 - Work with environments in Azure Machine Learning</h3> <ul> <li>Understand environments</li> <li>Explore and use curated environments</li> <li>Create and use custom environments</li> </ul> <h3>10 - Find the best classification model with Automated Machine Learning</h3> <ul> <li>Preprocess data and configure featurization</li> <li>Run an Automated Machine Learning experiment</li> <li>Evaluate and compare models</li> </ul> <h3>11 - Track model training in Jupyter notebooks with MLflow</h3> <ul> <li>Configure MLflow for model tracking in notebooks</li> <li>Train and track models in notebooks</li> </ul> <h3>12 - Run a training script as a command job in Azure Machine Learning</h3> <ul> <li>Convert a notebook to a script</li> <li>Run a script as a command job</li> <li>Use parameters in a command job</li> </ul> <h3>13 - Track model training with MLflow in jobs</h3> <ul> <li>Track metrics with MLflow</li> <li>View metrics and evaluate models</li> </ul> <h3>14 - Perform hyperparameter tuning with Azure Machine Learning</h3> <ul> <li>Define a search space</li> <li>Configure a sampling method</li> <li>Configure early termination</li> <li>Use a sweep job for hyperparameter tuning</li> </ul> <h3>15 - Run pipelines in Azure Machine Learning</h3> <ul> <li>Create components</li> <li>Create a pipeline</li> <li>Run a pipeline job</li> </ul> <h3>16 - Register an MLflow model in Azure Machine Learning</h3> <ul> <li>Log models with MLflow</li> <li>Understand the MLflow model format</li> <li>Register an MLflow model</li> </ul> <h3>17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning</h3> <ul> <li>Understand Responsible AI</li> <li>Create the Responsible AI dashboard</li> <li>Evaluate the Responsible AI dashboard</li> </ul> <h3>18 - Deploy a model to a managed online endpoint</h3> <ul> <li>Explore managed online endpoints</li> <li>Deploy your MLflow model to a managed online endpoint</li> <li>Deploy a model to a managed online endpoint</li> <li>Test managed online endpoints</li> </ul> <h3>19 - Deploy a model to a batch endpoint</h3> <ul> <li>Understand and create batch endpoints</li> <li>Deploy your MLflow model to a batch endpoint</li> <li>Deploy a custom model to a batch endpoint</li> <li>Invoke and troubleshoot batch endpoints</li> </ul></div>

Duration
4 Days
24 CPD hours
This course is intended for

This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.

Overview
Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.

Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.

Prerequisites

Creating cloud resources in Microsoft Azure.
Using Python to explore and visualize data.
Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.
Working with containers
AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience.

1 - Design a data ingestion strategy for machine learning projects
Identify your data source and format
Choose how to serve data to machine learning workflows
Design a data ingestion solution
2 - Design a machine learning model training solution
Identify machine learning tasks
Choose a service to train a machine learning model
Decide between compute options
3 - Design a model deployment solution
Understand how model will be consumed
Decide on real-time or batch deployment
4 - Design a machine learning operations solution
Explore an MLOps architecture
Design for monitoring
Design for retraining
5 - Explore Azure Machine Learning workspace resources and assets
Create an Azure Machine Learning workspace
Identify Azure Machine Learning resources
Identify Azure Machine Learning assets
Train models in the workspace
6 - Explore developer tools for workspace interaction
Explore the studio
Explore the Python SDK
Explore the CLI
7 - Make data available in Azure Machine Learning
Understand URIs
Create a datastore
Create a data asset
8 - Work with compute targets in Azure Machine Learning
Choose the appropriate compute target
Create and use a compute instance
Create and use a compute cluster
9 - Work with environments in Azure Machine Learning
Understand environments
Explore and use curated environments
Create and use custom environments
10 - Find the best classification model with Automated Machine Learning
Preprocess data and configure featurization
Run an Automated Machine Learning experiment
Evaluate and compare models
11 - Track model training in Jupyter notebooks with MLflow
Configure MLflow for model tracking in notebooks
Train and track models in notebooks
12 - Run a training script as a command job in Azure Machine Learning
Convert a notebook to a script
Run a script as a command job
Use parameters in a command job
13 - Track model training with MLflow in jobs
Track metrics with MLflow
View metrics and evaluate models
14 - Perform hyperparameter tuning with Azure Machine Learning
Define a search space
Configure a sampling method
Configure early termination
Use a sweep job for hyperparameter tuning
15 - Run pipelines in Azure Machine Learning
Create components
Create a pipeline
Run a pipeline job
16 - Register an MLflow model in Azure Machine Learning
Log models with MLflow
Understand the MLflow model format
Register an MLflow model
17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning
Understand Responsible AI
Create the Responsible AI dashboard
Evaluate the Responsible AI dashboard
18 - Deploy a model to a managed online endpoint
Explore managed online endpoints
Deploy your MLflow model to a managed online endpoint
Deploy a model to a managed online endpoint
Test managed online endpoints
19 - Deploy a model to a batch endpoint
Understand and create batch endpoints
Deploy your MLflow model to a batch endpoint
Deploy a custom model to a batch endpoint
Invoke and troubleshoot batch endpoints

Data Engineering

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

Azure Machine Learning

DP-100T01 Designing and Implementing a Data Science Solution on Azure

General Admission

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts</p>Overview<p>This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course.<br />Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below<br />Getting Started &amp; Optional Python Quick Refresher<br />Statistics and Probability Refresher and Python Practice<br />Probability Density Function; Probability Mass Function; Naive Bayes<br />Predictive Models<br />Machine Learning with Python<br />Recommender Systems<br />KNN and PCA<br />Reinforcement Learning<br />Dealing with Real-World Data<br />Experimental Design / ML in the Real World<br />Time Permitting: Deep Learning and Neural Networks</p><p>Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices.  This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning.  Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then  explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms</p></div><div id="course-pills-2"><h4>Getting Started</h4><ul><li>Installation: Getting Started and Overview</li><li>LINUX jump start: Installing and Using Anaconda &amp; Course Materials (or reference the default container)</li><li>Python Refresher</li><li>Introducing the Pandas, NumPy and Scikit-Learn Library</li></ul><h4>Statistics and Probability Refresher and Python Practice</h4><ul><li>Types of Data</li><li>Mean, Median, Mode</li><li>Using mean, median, and mode in Python</li><li>Variation and Standard Deviation</li></ul><h4>Probability Density Function; Probability Mass Function; Naive Bayes</h4><ul><li>Common Data Distributions</li><li>Percentiles and Moments</li><li>A Crash Course in matplotlib</li><li>Advanced Visualization with Seaborn</li><li>Covariance and Correlation</li><li>Conditional Probability</li><li>Naive Bayes: Concepts</li><li>Bayes? Theorem</li><li>Naive Bayes</li><li>Spam Classifier with Naive Bayes</li></ul><h4>Predictive Models</h4><ul><li>Linear Regression </li><li>Polynomial Regression</li><li>Multiple Regression, and Predicting Car Prices </li><li>Logistic Regression</li><li>Logistic Regression</li></ul><h4>Machine Learning with Python</h4><ul><li>Supervised vs. Unsupervised Learning, and Train/Test</li><li>Using Train/Test to Prevent Overfitting</li><li>Understanding a Confusion Matrix</li><li>Measuring Classifiers (Precision, Recall, F1, AUC, ROC)</li><li>K-Means Clustering</li><li>K-Means: Clustering People Based on Age and Income</li><li>Measuring Entropy</li><li>LINUX: Installing GraphViz</li><li>Decision Trees: Concepts</li><li>Decision Trees: Predicting Hiring Decisions</li><li>Ensemble Learning</li><li>Support Vector Machines (SVM) Overview</li><li>Using SVM to Cluster People using scikit-learn</li></ul><h4>Recommender Systems</h4><ul><li>User-Based Collaborative Filtering</li><li>Item-Based Collaborative Filtering</li><li>Finding Similar Movie</li><li>Better Accuracy for Similar Movies</li><li>Recommending movies to People</li><li>Improving your recommendations</li></ul><h4>KNN and PCA</h4><ul><li>K-Nearest-Neighbors: Concepts</li><li>Using KNN to Predict a Rating for a Movie</li><li>Dimensionality Reduction; Principal Component Analysis (PCA)</li><li>PCA with the Iris Data Set</li></ul><h4>Reinforcement Learning</h4><ul><li>Reinforcement Learning with Q-Learning and Gym</li></ul><h4>Dealing with Real-World Data</h4><ul><li>Bias / Variance Tradeoff</li><li>K-Fold Cross-Validation</li><li>Data Cleaning and Normalization</li><li>Cleaning Web Log Data</li><li>Normalizing Numerical Data</li><li>Detecting Outliers</li><li>Feature Engineering and the Curse of Dimensionality</li><li>Imputation Techniques for Missing Data</li><li>Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE</li><li>Binning, Transforming, Encoding, Scaling, and Shuffling</li></ul><h4>Experimental Design / ML in the Real World</h4><ul><li>Deploying Models to Real-Time Systems</li><li>A/B Testing Concepts</li><li>T-Tests and P-Values</li><li>Hands-on With T-Tests</li><li>Determining How Long to Run an Experiment</li><li>A/B Test Gotchas</li></ul><h4>Capstone Project</h4><ul><li>Group Project &amp; Presentation or Review</li></ul><h4>Deep Learning and Neural Networks</h4><ul><li>Deep Learning Prerequisites</li><li>The History of Artificial Neural Networks</li><li>Deep Learning in the TensorFlow Playground</li><li>Deep Learning Details</li><li>Introducing TensorFlow</li><li>Using TensorFlow</li><li>Introducing Keras</li><li>Using Keras to Predict Political Affiliations</li><li>Convolutional Neural Networks (CNN?s)</li><li>Using CNN?s for Handwriting Recognition</li><li>Recurrent Neural Networks (RNN?s)</li><li>Using an RNN for Sentiment Analysis</li><li>Transfer Learning</li><li>Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters</li><li>Deep Learning Regularization with Dropout and Early Stopping</li><li>The Ethics of Deep Learning</li><li>Learning More about Deep Learning</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Machine Learning Essentials with Python (TTML5506-P) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the Machine Learning Essentials with Python (TTML5506-P) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Duration
3 Days
18 CPD hours
This course is intended for
This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts
Overview
This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course.
Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below
Getting Started & Optional Python Quick Refresher
Statistics and Probability Refresher and Python Practice
Probability Density Function; Probability Mass Function; Naive Bayes
Predictive Models
Machine Learning with Python
Recommender Systems
KNN and PCA
Reinforcement Learning
Dealing with Real-World Data
Experimental Design / ML in the Real World
Time Permitting: Deep Learning and Neural Networks
Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms
Getting Started
Installation: Getting Started and Overview
LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container)
Python Refresher
Introducing the Pandas, NumPy and Scikit-Learn Library
Statistics and Probability Refresher and Python Practice
Types of Data
Mean, Median, Mode
Using mean, median, and mode in Python
Variation and Standard Deviation
Probability Density Function; Probability Mass Function; Naive Bayes
Common Data Distributions
Percentiles and Moments
A Crash Course in matplotlib
Advanced Visualization with Seaborn
Covariance and Correlation
Conditional Probability
Naive Bayes: Concepts
Bayes? Theorem
Naive Bayes
Spam Classifier with Naive Bayes
Predictive Models
Linear Regression
Polynomial Regression
Multiple Regression, and Predicting Car Prices
Logistic Regression
Logistic Regression
Machine Learning with Python
Supervised vs. Unsupervised Learning, and Train/Test
Using Train/Test to Prevent Overfitting
Understanding a Confusion Matrix
Measuring Classifiers (Precision, Recall, F1, AUC, ROC)
K-Means Clustering
K-Means: Clustering People Based on Age and Income
Measuring Entropy
LINUX: Installing GraphViz
Decision Trees: Concepts
Decision Trees: Predicting Hiring Decisions
Ensemble Learning
Support Vector Machines (SVM) Overview
Using SVM to Cluster People using scikit-learn
Recommender Systems
User-Based Collaborative Filtering
Item-Based Collaborative Filtering
Finding Similar Movie
Better Accuracy for Similar Movies
Recommending movies to People
Improving your recommendations
KNN and PCA
K-Nearest-Neighbors: Concepts
Using KNN to Predict a Rating for a Movie
Dimensionality Reduction; Principal Component Analysis (PCA)
PCA with the Iris Data Set
Reinforcement Learning
Reinforcement Learning with Q-Learning and Gym
Dealing with Real-World Data
Bias / Variance Tradeoff
K-Fold Cross-Validation
Data Cleaning and Normalization
Cleaning Web Log Data
Normalizing Numerical Data
Detecting Outliers
Feature Engineering and the Curse of Dimensionality
Imputation Techniques for Missing Data
Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE
Binning, Transforming, Encoding, Scaling, and Shuffling
Experimental Design / ML in the Real World
Deploying Models to Real-Time Systems
A/B Testing Concepts
T-Tests and P-Values
Hands-on With T-Tests
Determining How Long to Run an Experiment
A/B Test Gotchas
Capstone Project
Group Project & Presentation or Review
Deep Learning and Neural Networks
Deep Learning Prerequisites
The History of Artificial Neural Networks
Deep Learning in the TensorFlow Playground
Deep Learning Details
Introducing TensorFlow
Using TensorFlow
Introducing Keras
Using Keras to Predict Political Affiliations
Convolutional Neural Networks (CNN?s)
Using CNN?s for Handwriting Recognition
Recurrent Neural Networks (RNN?s)
Using an RNN for Sentiment Analysis
Transfer Learning
Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters
Deep Learning Regularization with Dropout and Early Stopping
The Ethics of Deep Learning
Learning More about Deep Learning
Additional course details:
Nexus Humans Machine Learning Essentials with Python (TTML5506-P) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. 

This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts.

Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.

While we feel this is the best course for the Machine Learning Essentials with Python (TTML5506-P) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.

Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.


Learning & Development

Managed Learning Programme (MLP)

Machine Learning (ML)

NLP Practitioner

Machine Learning Essentials with Python (TTML5506-P)

<div id="course-pills-1">Duration<p>1 Days</p><p>6 CPD hours</p>This course is intended for<p>This class is intended for the following:<br />Data analysts, Data scientists, Business analysts getting started with Google Cloud Platform.<br />Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports.<br />Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists.</p>Overview<p>This course teaches students the following skills:Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform.Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform.Employ BigQuery and Cloud Datalab to carry out interactive data analysis.Train and use a neural network using TensorFlow.Employ ML APIs.Choose between different data processing products on the Google Cloud Platform.</p><p>This course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities.</p></div><div id="course-pills-2"><h4>Introducing Google Cloud Platform</h4><ul><li>Google Platform Fundamentals Overview.</li><li>Google Cloud Platform Big Data Products.</li><li></li></ul><h4>Compute and Storage Fundamentals</h4><ul><li>CPUs on demand (Compute Engine).</li><li>A global filesystem (Cloud Storage).</li><li>CloudShell.</li><li>Lab: Set up a Ingest-Transform-Publish data processing pipeline.</li><li></li></ul><h4>Data Analytics on the Cloud</h4><ul><li>Stepping-stones to the cloud.</li><li>Cloud SQL: your SQL database on the cloud.</li><li>Lab: Importing data into CloudSQL and running queries.</li><li>Spark on Dataproc.</li><li>Lab: Machine Learning Recommendations with Spark on Dataproc.</li><li></li></ul><h4>Scaling Data Analysis</h4><ul><li>Fast random access.</li><li>Datalab.</li><li>BigQuery.</li><li>Lab: Build machine learning dataset.</li><li></li></ul><h4>Machine Learning</h4><ul><li>Machine Learning with TensorFlow.</li><li>Lab: Carry out ML with TensorFlow</li><li>Pre-built models for common needs.</li><li>Lab: Employ ML APIs.</li><li></li></ul><h4>Data Processing Architectures</h4><ul><li>Message-oriented architectures with Pub/Sub.</li><li>Creating pipelines with Dataflow.</li><li>Reference architecture for real-time and batch data processing.</li><li></li></ul><h4>Summary</h4><ul><li>Why GCP?</li><li>Where to go from here</li><li>Additional Resources     </li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Google Cloud Platform Big Data and Machine Learning Fundamentals training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the Google Cloud Platform Big Data and Machine Learning Fundamentals course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Duration
1 Days
6 CPD hours
This course is intended for
This class is intended for the following:
Data analysts, Data scientists, Business analysts getting started with Google Cloud Platform.
Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports.
Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists.
Overview
This course teaches students the following skills:Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform.Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform.Employ BigQuery and Cloud Datalab to carry out interactive data analysis.Train and use a neural network using TensorFlow.Employ ML APIs.Choose between different data processing products on the Google Cloud Platform.
This course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities.
Introducing Google Cloud Platform
Google Platform Fundamentals Overview.
Google Cloud Platform Big Data Products.

Compute and Storage Fundamentals
CPUs on demand (Compute Engine).
A global filesystem (Cloud Storage).
CloudShell.
Lab: Set up a Ingest-Transform-Publish data processing pipeline.

Data Analytics on the Cloud
Stepping-stones to the cloud.
Cloud SQL: your SQL database on the cloud.
Lab: Importing data into CloudSQL and running queries.
Spark on Dataproc.
Lab: Machine Learning Recommendations with Spark on Dataproc.

Scaling Data Analysis
Fast random access.
Datalab.
BigQuery.
Lab: Build machine learning dataset.

Machine Learning
Machine Learning with TensorFlow.
Lab: Carry out ML with TensorFlow
Pre-built models for common needs.
Lab: Employ ML APIs.

Data Processing Architectures
Message-oriented architectures with Pub/Sub.
Creating pipelines with Dataflow.
Reference architecture for real-time and batch data processing.

Summary
Why GCP?
Where to go from here
Additional Resources
Additional course details:
Nexus Humans Google Cloud Platform Big Data and Machine Learning Fundamentals training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. 

This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts.

Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.

While we feel this is the best course for the Google Cloud Platform Big Data and Machine Learning Fundamentals course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.

Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.


Google Data Analytics

Big Data Analytics

Google Cloud

Google Digital Marketing

Google Cloud Platform Big Data and Machine Learning Fundamentals

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>Attendee roles might include:<br />Software Developers/Programmers<br />Data Scientists<br />Machine Learning Engineers<br />AI Researchers<br />User Interface (UI) and User Experience (UX) Designers<br />Technical Product Managers<br />Technical Team Leads</p>Overview<p>Working in an interactive learning environment, led by our engaging AI expert you'll:<br />Develop a strong foundational understanding of generative AI techniques and their applications in software development.<br />Gain hands-on experience working with popular generative AI models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models.<br />Master the use of leading AI libraries and frameworks, such as TensorFlow, Keras, and Hugging Face Transformers, for implementing generative AI models.<br />Acquire the skills to design, train, optimize, and evaluate custom generative AI models tailored to specific software development tasks.<br />Learn to fine-tune pre-trained generative AI models for targeted applications and deploy them effectively in various environments, including cloud-based services and on-premises servers.<br />Understand and address the ethical, legal, and safety considerations of using generative AI, including mitigating biases and ensuring responsible AI-generated content.</p><p>Prompt Engineering offers coders and software developers a competitive edge by empowering them to develop more effective and efficient AI-driven solutions in their projects. By harnessing the capabilities of cutting-edge AI models like GPT-4, coders can automate repetitive tasks, enhance natural language understanding, and even generate code suggestions, boosting productivity and creativity. In addition, mastering prompt engineering can contribute to improved job security, as professionals with these in-demand skills are highly sought after in the rapidly evolving tech landscape. Quick Start to Prompt Engineering for Coders and Software Developers is a one day course designed to get you quickly up and running with the prompting skills required to out AI to work for you in your development efforts.  Guided by our AI expert, you?ll explore key topics such as text preprocessing, data cleansing, GPT-4 tokenization, input formatting, prompt design, and optimization, as well as ethical considerations in prompt engineering. In the hands-on labs you?ll explore tasks such as formatting inputs for GPT-4, designing and optimizing prompts for business applications, and implementing multi-turn conversations with AI. You?ll work with innovative tools like the OpenAI API, OpenAI Codex, and OpenAI Playground, enhancing your learning experience while preparing you for integrating prompt engineering into your professional toolkit. By the end of this immersive course, you?ll have the skills necessary to effectively use prompt engineering in your software development projects. You'll be able to design, optimize, and test prompts for various business tasks, integrate GPT-4 with other software platforms, and address ethical concerns in AI deployment.</p></div><div id="course-pills-2">Generative AI represents an exhilarating frontier in artificial intelligence, specializing in the creation of new data instances, imitation of real data, and content generation. Its remarkable capabilities facilitate automated content creation, enriched user experiences, and groundbreaking solutions across diverse industries, ultimately fueling efficiency and transcending technological limits. By harnessing the power of generative AI, developers can craft dynamic content, produce code and documentation, refine user interfaces, and devise customized recommendations, empowering them to construct highly efficient and custom solutions for a wide range of applications. Designed for experienced programmers, Turbocharge Your Code! Generative AI Boot Camp for Developers is a three-day workshop-style course that teaches you the latest skills and tools required to master generative AI models, transforming the way you approach software development. In today's fast-paced technological landscape, generative AI has emerged as a game-changer, with leading companies like NVIDIA, OpenAI, and Google leveraging its capabilities to push the boundaries of innovation. By learning how to harness the power of generative models such as GANs, VAEs, and Transformer models, you will be able to generate code, documentation, and tests, enhance user interfaces, and create dynamic content that adapts to user needs. Our comprehensive curriculum covers everything from the fundamentals of generative AI to advanced techniques and ethical considerations, including hands-on labs where you will develop and deploy custom models using state-of-the-art AI tools and libraries like TensorFlow and Hugging Face Transformers. Throughout the course you'll focus on practical application and collaboration, building confidence with personalized guidance and real-time feedback from our expert live instructor. Upon completion, you will be equipped with the knowledge and experience necessary to develop and implement innovative generative AI models across various industries, improving existing products, creating new applications, and gaining highly-valuable skills in the rapidly advancing field of AI.</div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Turbocharge Your Code! Generative AI Boot Camp for Developers  (TTAI2305) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the Turbocharge Your Code! Generative AI Boot Camp for Developers  (TTAI2305) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Duration
3 Days
18 CPD hours
This course is intended for
Attendee roles might include:
Software Developers/Programmers
Data Scientists
Machine Learning Engineers
AI Researchers
User Interface (UI) and User Experience (UX) Designers
Technical Product Managers
Technical Team Leads
Overview
Working in an interactive learning environment, led by our engaging AI expert you'll:
Develop a strong foundational understanding of generative AI techniques and their applications in software development.
Gain hands-on experience working with popular generative AI models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models.
Master the use of leading AI libraries and frameworks, such as TensorFlow, Keras, and Hugging Face Transformers, for implementing generative AI models.
Acquire the skills to design, train, optimize, and evaluate custom generative AI models tailored to specific software development tasks.
Learn to fine-tune pre-trained generative AI models for targeted applications and deploy them effectively in various environments, including cloud-based services and on-premises servers.
Understand and address the ethical, legal, and safety considerations of using generative AI, including mitigating biases and ensuring responsible AI-generated content.
Prompt Engineering offers coders and software developers a competitive edge by empowering them to develop more effective and efficient AI-driven solutions in their projects. By harnessing the capabilities of cutting-edge AI models like GPT-4, coders can automate repetitive tasks, enhance natural language understanding, and even generate code suggestions, boosting productivity and creativity. In addition, mastering prompt engineering can contribute to improved job security, as professionals with these in-demand skills are highly sought after in the rapidly evolving tech landscape. Quick Start to Prompt Engineering for Coders and Software Developers is a one day course designed to get you quickly up and running with the prompting skills required to out AI to work for you in your development efforts. Guided by our AI expert, you?ll explore key topics such as text preprocessing, data cleansing, GPT-4 tokenization, input formatting, prompt design, and optimization, as well as ethical considerations in prompt engineering. In the hands-on labs you?ll explore tasks such as formatting inputs for GPT-4, designing and optimizing prompts for business applications, and implementing multi-turn conversations with AI. You?ll work with innovative tools like the OpenAI API, OpenAI Codex, and OpenAI Playground, enhancing your learning experience while preparing you for integrating prompt engineering into your professional toolkit. By the end of this immersive course, you?ll have the skills necessary to effectively use prompt engineering in your software development projects. You'll be able to design, optimize, and test prompts for various business tasks, integrate GPT-4 with other software platforms, and address ethical concerns in AI deployment.
Generative AI represents an exhilarating frontier in artificial intelligence, specializing in the creation of new data instances, imitation of real data, and content generation. Its remarkable capabilities facilitate automated content creation, enriched user experiences, and groundbreaking solutions across diverse industries, ultimately fueling efficiency and transcending technological limits. By harnessing the power of generative AI, developers can craft dynamic content, produce code and documentation, refine user interfaces, and devise customized recommendations, empowering them to construct highly efficient and custom solutions for a wide range of applications. Designed for experienced programmers, Turbocharge Your Code! Generative AI Boot Camp for Developers is a three-day workshop-style course that teaches you the latest skills and tools required to master generative AI models, transforming the way you approach software development. In today's fast-paced technological landscape, generative AI has emerged as a game-changer, with leading companies like NVIDIA, OpenAI, and Google leveraging its capabilities to push the boundaries of innovation. By learning how to harness the power of generative models such as GANs, VAEs, and Transformer models, you will be able to generate code, documentation, and tests, enhance user interfaces, and create dynamic content that adapts to user needs. Our comprehensive curriculum covers everything from the fundamentals of generative AI to advanced techniques and ethical considerations, including hands-on labs where you will develop and deploy custom models using state-of-the-art AI tools and libraries like TensorFlow and Hugging Face Transformers. Throughout the course you'll focus on practical application and collaboration, building confidence with personalized guidance and real-time feedback from our expert live instructor. Upon completion, you will be equipped with the knowledge and experience necessary to develop and implement innovative generative AI models across various industries, improving existing products, creating new applications, and gaining highly-valuable skills in the rapidly advancing field of AI.
Additional course details:
Nexus Humans Turbocharge Your Code! Generative AI Boot Camp for Developers (TTAI2305) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. 

This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts.

Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.

While we feel this is the best course for the Turbocharge Your Code! Generative AI Boot Camp for Developers (TTAI2305) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.

Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.


Generative Design

Generative AI

AI Art Generation

Generative Adversarial Networks (GAN)

AI (Artificial Intelligence)

Turbocharge Your Code! Generative AI Boot Camp for Developers  (TTAI2305)

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>This course is geared for Python experienced attendees who wish to learn and use basic machine learning algorithms and concepts. Students should have skills at least equivalent to the Python for Data Science courses we offer.</p>Overview<p>Working in a hands-on learning environment, guided by our expert team, attendees will learn to<br />Understand the main concepts and principles of predictive analytics<br />Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects<br />Explore advanced predictive modeling algorithms w with an emphasis on theory with intuitive explanations<br />Learn to deploy a predictive model's results as an interactive application<br />Learn about the stages involved in producing complete predictive analytics solutions<br />Understand how to define a problem, propose a solution, and prepare a dataset<br />Use visualizations to explore relationships and gain insights into the dataset<br />Learn to build regression and classification models using scikit-learn<br />Use Keras to build powerful neural network models that produce accurate predictions<br />Learn to serve a model's predictions as a web application</p><p>Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This course provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. Hands-on Predictive Analytics with Python is a three-day, hands-on course that guides students through a step-by-step approach to defining problems and identifying relevant data. Students will learn how to perform data preparation, explore and visualize relationships, as well as build models, tune, evaluate, and deploy models.  Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seabor, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics.</p></div><div id="course-pills-2"><h4>The Predictive Analytics Process</h4><ul><li>Technical requirements</li><li>What is predictive analytics?</li><li>Reviewing important concepts of predictive analytics</li><li>The predictive analytics process</li><li>A quick tour of Python's data science stack</li></ul><h4>Problem Understanding and Data Preparation</h4><ul><li>Technical requirements</li><li>Understanding the business problem and proposing a solution</li><li>Practical project ? diamond prices</li><li>Practical project ? credit card default</li></ul><h4>Dataset Understanding ? Exploratory Data Analysis</h4><ul><li>Technical requirements</li><li>What is EDA?</li><li>Univariate EDA</li><li>Bivariate EDA</li><li>Introduction to graphical multivariate EDA</li></ul><h4>Predicting Numerical Values with Machine Learning</h4><ul><li>Technical requirements</li><li>Introduction to ML</li><li>Practical considerations before modeling</li><li>MLR</li><li>Lasso regression</li><li>KNN</li><li>Training versus testing error</li></ul><h4>Predicting Categories with Machine Learning</h4><ul><li>Technical requirements</li><li>Classification tasks</li><li>Credit card default dataset</li><li>Logistic regression</li><li>Classification trees</li><li>Random forests</li><li>Training versus testing error</li><li>Multiclass classification</li><li>Naive Bayes classifiers</li></ul><h4>Introducing Neural Nets for Predictive Analytics</h4><ul><li>Technical requirements</li><li>Introducing neural network models</li><li>Introducing TensorFlow and Keras</li><li>Regressing with neural networks</li><li>Classification with neural networks</li><li>The dark art of training neural networks</li></ul><h4>Model Evaluation</h4><ul><li>Technical requirements</li><li>Evaluation of regression models</li><li>Evaluation for classification models</li><li>The k-fold cross-validation</li></ul><h4>Model Tuning and Improving Performance</h4><ul><li>Technical requirements</li><li>Hyperparameter tuning</li><li>Improving performance</li></ul><h4>Implementing a Model with Dash</h4><ul><li>Technical requirements</li><li>Model communication and/or deployment phase</li><li>Introducing Dash</li><li>Implementing a predictive model as a web application</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Hands-on Predicitive Analytics with Python (TTPS4879) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the Hands-on Predicitive Analytics with Python (TTPS4879) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Duration
3 Days
18 CPD hours
This course is intended for
This course is geared for Python experienced attendees who wish to learn and use basic machine learning algorithms and concepts. Students should have skills at least equivalent to the Python for Data Science courses we offer.
Overview
Working in a hands-on learning environment, guided by our expert team, attendees will learn to
Understand the main concepts and principles of predictive analytics
Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects
Explore advanced predictive modeling algorithms w with an emphasis on theory with intuitive explanations
Learn to deploy a predictive model's results as an interactive application
Learn about the stages involved in producing complete predictive analytics solutions
Understand how to define a problem, propose a solution, and prepare a dataset
Use visualizations to explore relationships and gain insights into the dataset
Learn to build regression and classification models using scikit-learn
Use Keras to build powerful neural network models that produce accurate predictions
Learn to serve a model's predictions as a web application
Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This course provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. Hands-on Predictive Analytics with Python is a three-day, hands-on course that guides students through a step-by-step approach to defining problems and identifying relevant data. Students will learn how to perform data preparation, explore and visualize relationships, as well as build models, tune, evaluate, and deploy models. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seabor, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics.
The Predictive Analytics Process
Technical requirements
What is predictive analytics?
Reviewing important concepts of predictive analytics
The predictive analytics process
A quick tour of Python's data science stack
Problem Understanding and Data Preparation
Technical requirements
Understanding the business problem and proposing a solution
Practical project ? diamond prices
Practical project ? credit card default
Dataset Understanding ? Exploratory Data Analysis
Technical requirements
What is EDA?
Univariate EDA
Bivariate EDA
Introduction to graphical multivariate EDA
Predicting Numerical Values with Machine Learning
Technical requirements
Introduction to ML
Practical considerations before modeling
MLR
Lasso regression
KNN
Training versus testing error
Predicting Categories with Machine Learning
Technical requirements
Classification tasks
Credit card default dataset
Logistic regression
Classification trees
Random forests
Training versus testing error
Multiclass classification
Naive Bayes classifiers
Introducing Neural Nets for Predictive Analytics
Technical requirements
Introducing neural network models
Introducing TensorFlow and Keras
Regressing with neural networks
Classification with neural networks
The dark art of training neural networks
Model Evaluation
Technical requirements
Evaluation of regression models
Evaluation for classification models
The k-fold cross-validation
Model Tuning and Improving Performance
Technical requirements
Hyperparameter tuning
Improving performance
Implementing a Model with Dash
Technical requirements
Model communication and/or deployment phase
Introducing Dash
Implementing a predictive model as a web application
Additional course details:
Nexus Humans Hands-on Predicitive Analytics with Python (TTPS4879) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. 

This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts.

Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.

While we feel this is the best course for the Hands-on Predicitive Analytics with Python (TTPS4879) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.

Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.


Predictive Analytics

Business Analytics & Intelligence

Data Science

Data Skills

Hands-on Predicitive Analytics with Python (TTPS4879)

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p>This class is intended for experienced developers who are responsible for managing big data transformations including:<br />Extracting, loading, transforming, cleaning, and validating data.<br />Designing pipelines and architectures for data processing.<br />Creating and maintaining machine learning and statistical models.<br />Querying datasets, visualizing query results and creating reports</p>Overview<p>Design and build data processing systems on Google Cloud Platform.<br />Leverage unstructured data using Spark and ML APIs on Cloud Dataproc.<br />Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow.<br />Derive business insights from extremely large datasets using Google BigQuery.<br />Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML.<br />Enable instant insights from streaming data</p><p>Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data.</p></div><div id="course-pills-2"><h4>Introduction to Data Engineering</h4><ul><li>Explore the role of a data engineer.</li><li>Analyze data engineering challenges.</li><li>Intro to BigQuery.</li><li>Data Lakes and Data Warehouses.</li><li>Demo: Federated Queries with BigQuery.</li><li>Transactional Databases vs Data Warehouses.</li><li>Website Demo: Finding PII in your dataset with DLP API.</li><li>Partner effectively with other data teams.</li><li>Manage data access and governance.</li><li>Build production-ready pipelines.</li><li>Review GCP customer case study.</li><li>Lab: Analyzing Data with BigQuery.</li></ul><h4>Building a Data Lake</h4><ul><li>Introduction to Data Lakes.</li><li>Data Storage and ETL options on GCP.</li><li>Building a Data Lake using Cloud Storage.</li><li>Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions.</li><li>Securing Cloud Storage.</li><li>Storing All Sorts of Data Types.</li><li>Video Demo: Running federated queries on Parquet and ORC files in BigQuery.</li><li>Cloud SQL as a relational Data Lake.</li><li>Lab: Loading Taxi Data into Cloud SQL.</li></ul><h4>Building a Data Warehouse</h4><ul><li>The modern data warehouse.</li><li>Intro to BigQuery.</li><li>Demo: Query TB+ of data in seconds.</li><li>Getting Started.</li><li>Loading Data.</li><li>Video Demo: Querying Cloud SQL from BigQuery.</li><li>Lab: Loading Data into BigQuery.</li><li>Exploring Schemas.</li><li>Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA.</li><li>Schema Design.</li><li>Nested and Repeated Fields.</li><li>Demo: Nested and repeated fields in BigQuery.</li><li>Lab: Working with JSON and Array data in BigQuery.</li><li>Optimizing with Partitioning and Clustering.</li><li>Demo: Partitioned and Clustered Tables in BigQuery.</li><li>Preview: Transforming Batch and Streaming Data.</li></ul><h4>Introduction to Building Batch Data Pipelines</h4><ul><li>EL, ELT, ETL.</li><li>Quality considerations.</li><li>How to carry out operations in BigQuery.</li><li>Demo: ELT to improve data quality in BigQuery.</li><li>Shortcomings.</li><li>ETL to solve data quality issues.</li></ul><h4>Executing Spark on Cloud Dataproc</h4><ul><li>The Hadoop ecosystem.</li><li>Running Hadoop on Cloud Dataproc.</li><li>GCS instead of HDFS.</li><li>Optimizing Dataproc.</li><li>Lab: Running Apache Spark jobs on Cloud Dataproc.</li></ul><h4>Serverless Data Processing with Cloud Dataflow</h4><ul><li>Cloud Dataflow.</li><li>Why customers value Dataflow.</li><li>Dataflow Pipelines.</li><li>Lab: A Simple Dataflow Pipeline (Python/Java).</li><li>Lab: MapReduce in Dataflow (Python/Java).</li><li>Lab: Side Inputs (Python/Java).</li><li>Dataflow Templates.</li><li>Dataflow SQL.</li></ul><h4>Manage Data Pipelines with Cloud Data Fusion and Cloud Composer</h4><ul><li>Building Batch Data Pipelines visually with Cloud Data Fusion.</li><li>Components.</li><li>UI Overview.</li><li>Building a Pipeline.</li><li>Exploring Data using Wrangler.</li><li>Lab: Building and executing a pipeline graph in Cloud Data Fusion.</li><li>Orchestrating work between GCP services with Cloud Composer.</li><li>Apache Airflow Environment.</li><li>DAGs and Operators.</li><li>Workflow Scheduling.</li><li>Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery.</li><li>Monitoring and Logging.</li><li>Lab: An Introduction to Cloud Composer.</li></ul><h4>Introduction to Processing Streaming Data</h4><ul><li>Processing Streaming Data.</li></ul><h4>Serverless Messaging with Cloud Pub/Sub</h4><ul><li>Cloud Pub/Sub.</li><li>Lab: Publish Streaming Data into Pub/Sub.</li></ul><h4>Cloud Dataflow Streaming Features</h4><ul><li>Cloud Dataflow Streaming Features.</li><li>Lab: Streaming Data Pipelines.</li></ul><h4>High-Throughput BigQuery and Bigtable Streaming Features</h4><ul><li>BigQuery Streaming Features.</li><li>Lab: Streaming Analytics and Dashboards.</li><li>Cloud Bigtable.</li><li>Lab: Streaming Data Pipelines into Bigtable.</li></ul><h4>Advanced BigQuery Functionality and Performance</h4><ul><li>Analytic Window Functions.</li><li>Using With Clauses.</li><li>GIS Functions.</li><li>Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz.</li><li>Performance Considerations.</li><li>Lab: Optimizing your BigQuery Queries for Performance.</li><li>Optional Lab: Creating Date-Partitioned Tables in BigQuery.</li></ul><h4>Introduction to Analytics and AI</h4><ul><li>What is AI?.</li><li>From Ad-hoc Data Analysis to Data Driven Decisions.</li><li>Options for ML models on GCP.</li></ul><h4>Prebuilt ML model APIs for Unstructured Data</h4><ul><li>Unstructured Data is Hard.</li><li>ML APIs for Enriching Data.</li><li>Lab: Using the Natural Language API to Classify Unstructured Text.</li></ul><h4>Big Data Analytics with Cloud AI Platform Notebooks</h4><ul><li>What's a Notebook.</li><li>BigQuery Magic and Ties to Pandas.</li><li>Lab: BigQuery in Jupyter Labs on AI Platform.</li></ul><h4>Production ML Pipelines with Kubeflow</h4><ul><li>Ways to do ML on GCP.</li><li>Kubeflow.</li><li>AI Hub.</li><li>Lab: Running AI models on Kubeflow.</li></ul><h4>Custom Model building with SQL in BigQuery ML</h4><ul><li>BigQuery ML for Quick Model Building.</li><li>Demo: Train a model with BigQuery ML to predict NYC taxi fares.</li><li>Supported Models.</li><li>Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML.</li><li>Lab Option 2: Movie Recommendations in BigQuery ML.</li></ul><h4>Custom Model building with Cloud AutoML</h4><ul><li>Why Auto ML?</li><li>Auto ML Vision.</li><li>Auto ML NLP.</li><li>Auto ML Tables.</li></ul></div>

Duration
4 Days
24 CPD hours
This course is intended for
This class is intended for experienced developers who are responsible for managing big data transformations including:
Extracting, loading, transforming, cleaning, and validating data.
Designing pipelines and architectures for data processing.
Creating and maintaining machine learning and statistical models.
Querying datasets, visualizing query results and creating reports
Overview
Design and build data processing systems on Google Cloud Platform.
Leverage unstructured data using Spark and ML APIs on Cloud Dataproc.
Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow.
Derive business insights from extremely large datasets using Google BigQuery.
Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML.
Enable instant insights from streaming data
Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data.
Introduction to Data Engineering
Explore the role of a data engineer.
Analyze data engineering challenges.
Intro to BigQuery.
Data Lakes and Data Warehouses.
Demo: Federated Queries with BigQuery.
Transactional Databases vs Data Warehouses.
Website Demo: Finding PII in your dataset with DLP API.
Partner effectively with other data teams.
Manage data access and governance.
Build production-ready pipelines.
Review GCP customer case study.
Lab: Analyzing Data with BigQuery.
Building a Data Lake
Introduction to Data Lakes.
Data Storage and ETL options on GCP.
Building a Data Lake using Cloud Storage.
Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions.
Securing Cloud Storage.
Storing All Sorts of Data Types.
Video Demo: Running federated queries on Parquet and ORC files in BigQuery.
Cloud SQL as a relational Data Lake.
Lab: Loading Taxi Data into Cloud SQL.
Building a Data Warehouse
The modern data warehouse.
Intro to BigQuery.
Demo: Query TB+ of data in seconds.
Getting Started.
Loading Data.
Video Demo: Querying Cloud SQL from BigQuery.
Lab: Loading Data into BigQuery.
Exploring Schemas.
Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA.
Schema Design.
Nested and Repeated Fields.
Demo: Nested and repeated fields in BigQuery.
Lab: Working with JSON and Array data in BigQuery.
Optimizing with Partitioning and Clustering.
Demo: Partitioned and Clustered Tables in BigQuery.
Preview: Transforming Batch and Streaming Data.
Introduction to Building Batch Data Pipelines
EL, ELT, ETL.
Quality considerations.
How to carry out operations in BigQuery.
Demo: ELT to improve data quality in BigQuery.
Shortcomings.
ETL to solve data quality issues.
Executing Spark on Cloud Dataproc
The Hadoop ecosystem.
Running Hadoop on Cloud Dataproc.
GCS instead of HDFS.
Optimizing Dataproc.
Lab: Running Apache Spark jobs on Cloud Dataproc.
Serverless Data Processing with Cloud Dataflow
Cloud Dataflow.
Why customers value Dataflow.
Dataflow Pipelines.
Lab: A Simple Dataflow Pipeline (Python/Java).
Lab: MapReduce in Dataflow (Python/Java).
Lab: Side Inputs (Python/Java).
Dataflow Templates.
Dataflow SQL.
Manage Data Pipelines with Cloud Data Fusion and Cloud Composer
Building Batch Data Pipelines visually with Cloud Data Fusion.
Components.
UI Overview.
Building a Pipeline.
Exploring Data using Wrangler.
Lab: Building and executing a pipeline graph in Cloud Data Fusion.
Orchestrating work between GCP services with Cloud Composer.
Apache Airflow Environment.
DAGs and Operators.
Workflow Scheduling.
Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery.
Monitoring and Logging.
Lab: An Introduction to Cloud Composer.
Introduction to Processing Streaming Data
Processing Streaming Data.
Serverless Messaging with Cloud Pub/Sub
Cloud Pub/Sub.
Lab: Publish Streaming Data into Pub/Sub.
Cloud Dataflow Streaming Features
Cloud Dataflow Streaming Features.
Lab: Streaming Data Pipelines.
High-Throughput BigQuery and Bigtable Streaming Features
BigQuery Streaming Features.
Lab: Streaming Analytics and Dashboards.
Cloud Bigtable.
Lab: Streaming Data Pipelines into Bigtable.
Advanced BigQuery Functionality and Performance
Analytic Window Functions.
Using With Clauses.
GIS Functions.
Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz.
Performance Considerations.
Lab: Optimizing your BigQuery Queries for Performance.
Optional Lab: Creating Date-Partitioned Tables in BigQuery.
Introduction to Analytics and AI
What is AI?.
From Ad-hoc Data Analysis to Data Driven Decisions.
Options for ML models on GCP.
Prebuilt ML model APIs for Unstructured Data
Unstructured Data is Hard.
ML APIs for Enriching Data.
Lab: Using the Natural Language API to Classify Unstructured Text.
Big Data Analytics with Cloud AI Platform Notebooks
What's a Notebook.
BigQuery Magic and Ties to Pandas.
Lab: BigQuery in Jupyter Labs on AI Platform.
Production ML Pipelines with Kubeflow
Ways to do ML on GCP.
Kubeflow.
AI Hub.
Lab: Running AI models on Kubeflow.
Custom Model building with SQL in BigQuery ML
BigQuery ML for Quick Model Building.
Demo: Train a model with BigQuery ML to predict NYC taxi fares.
Supported Models.
Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML.
Lab Option 2: Movie Recommendations in BigQuery ML.
Custom Model building with Cloud AutoML
Why Auto ML?
Auto ML Vision.
Auto ML NLP.
Auto ML Tables.

Data Engineering on Google Cloud

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>The ideal audience for the RPA and UiPath Boot Camp is beginners in the field of RPA and individuals in roles such as developers, project managers, operation analysts, and tech enthusiasts looking to familiarize themselves with automation technologies. It's also perfectly suited for business professionals keen on understanding and implementing automated solutions within their organizations to optimize processes.</p>Overview<p>This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Working in a hands-on learning environment, led by our Automation Learning expert instructor, students will explore:<br />Gain a thorough understanding of Robotic Process Automation (RPA) and its applications using UiPath, setting a solid foundation for future learning and application.<br />Learn to record and play in UiPath Studio, a key skill that enables automating complex tasks in a user-friendly environment.<br />Master the art of designing and controlling workflows using Sequencing, Flowcharting, and Control Flow, helping to streamline and manage automation processes effectively.<br />Acquire practical skills in data manipulation, from variable management to CSV/Excel and data table conversions, empowering you to handle data-rich tasks with confidence.<br />Develop competence in managing controls and exploring various plugins and extensions, providing a broader toolkit for handling diverse automation projects.<br />Get hands-on experience with exception handling, debugging, logging, code management, and bot deployment, fundamental skills that ensure your automated processes are reliable and efficient.<br />How to deploy and control Bots with UiPath Orchestrator</p><p>The Hands-on Natural Language Processing (NLP) Boot Camp is an immersive, three-day course that serves as your guide to building machines that can read and interpret human language. NLP is a unique interdisciplinary field, blending computational linguistics with artificial intelligence to help machines understand, interpret, and generate human language. In an increasingly data-driven world, NLP skills provide a competitive edge, enabling the development of sophisticated projects such as voice assistants, text analyzers, chatbots, and so much more. Our comprehensive curriculum covers a broad spectrum of NLP topics. Beginning with an introduction to NLP and feature extraction, the course moves to the hands-on development of text classifiers, exploration of web scraping and APIs, before delving into topic modeling, vector representations, text manipulation, and sentiment analysis. Half of your time is dedicated to hands-on labs, where you'll experience the practical application of your knowledge, from creating pipelines and text classifiers to web scraping and analyzing sentiment. These labs serve as a microcosm of real-world scenarios, equipping you with the skills to efficiently process and analyze text data. Time permitting, you?ll also explore modern tools like Python libraries, the OpenAI GPT-3 API, and TensorFlow, using them in a series of engaging exercises. By the end of the course, you'll have a well-rounded understanding of NLP, and will leave equipped with the practical skills and insights that you can immediately put to use, helping your organization gain valuable insights from text data, streamline business processes, and improve user interactions with automated text-based systems. You?ll be able to process and analyze text data effectively, implement advanced text representations, apply machine learning algorithms for text data, and build simple chatbots.</p></div><div id="course-pills-2"><h4>What is Robotic Process  Automation?</h4><ul><li>Scope and techniques of automation </li><li>Robotic process automation </li><li>About UiPath </li><li>The future of automation</li></ul><h4>Record and Play</h4><ul><li>UiPath stack </li><li>Downloading and installing UiPath Studio </li><li>Learning UiPath Studio </li><li>Task recorder </li><li>Step-by-step examples using the recorder</li></ul><h4>Sequence, Flowchart, and Control Flow</h4><ul><li>Sequencing the workflow Activities </li><li>Control flow, various types of loops, and decision making </li><li>Step-by-step example using Sequence and Flowchart </li><li>Step-by-step example using Sequence and Control flow</li></ul><h4>Data Manipulation</h4><ul><li>Variables and scope </li><li>Collections </li><li>Arguments ? Purpose and use </li><li>Data table usage with examples </li><li>Clipboard management </li><li>File operation with step-by-step example </li><li>CSV/Excel to data table and vice versa (with a step-by-step example) </li></ul><h4>Taking Control of the Controls</h4><ul><li>Finding and attaching windows </li><li>Finding the control </li><li>Techniques for waiting for a control Act on controls ? mouse and keyboard activities </li><li>Working with UiExplorer </li><li>Handling events </li><li>Revisit recorder </li><li>Screen Scraping </li><li>When to use OCR </li><li>Types of OCR available </li><li>How to use OCR </li><li>Avoiding typical failure points</li></ul><h4>Tame that Application with Plugins and Extensions</h4><ul><li>Terminal plugin </li><li>SAP automation </li><li>Java plugin </li><li>Citrix automation </li><li>Mail plugin </li><li>PDF plugin </li><li>Web integration </li><li>Excel and Word plugins </li><li>Credential management Extensions ? Java, Chrome, Firefox, and Silverlight</li></ul><h4>Handling User Events and Assistant Bots</h4><ul><li>What are assistant bots? </li><li>Monitoring system event triggers </li><li>Monitoring image and element triggers </li><li>Launching an assistant bot on a keyboard event</li></ul><h4>Exception Handling, Debugging, and Logging</h4><ul><li>Exception handling </li><li>Common exceptions and ways to handle them </li><li>Logging and taking screenshots </li><li>Debugging techniques </li><li>Collecting crash dumps </li><li>Error reporting </li></ul><h4>Managing and Maintaining the Code</h4><ul><li>Project organization </li><li>Nesting workflows </li><li>Reusability of workflows </li><li>Commenting techniques </li><li>State Machine </li><li>When to use Flowcharts, State Machines, or Sequences Using config files and examples of a config file </li><li>Integrating a TFS server</li></ul><h4>Deploying and Maintaining the Bot</h4><ul><li>Publishing using publish utility </li><li>Overview of Orchestration Server </li><li>Using Orchestration Server to control bots </li><li>Using Orchestration Server to deploy bots </li><li>License management </li><li>Publishing and managing updates</li></ul></div>

Duration
3 Days
18 CPD hours
This course is intended for
The ideal audience for the RPA and UiPath Boot Camp is beginners in the field of RPA and individuals in roles such as developers, project managers, operation analysts, and tech enthusiasts looking to familiarize themselves with automation technologies. It's also perfectly suited for business professionals keen on understanding and implementing automated solutions within their organizations to optimize processes.
Overview
This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Working in a hands-on learning environment, led by our Automation Learning expert instructor, students will explore:
Gain a thorough understanding of Robotic Process Automation (RPA) and its applications using UiPath, setting a solid foundation for future learning and application.
Learn to record and play in UiPath Studio, a key skill that enables automating complex tasks in a user-friendly environment.
Master the art of designing and controlling workflows using Sequencing, Flowcharting, and Control Flow, helping to streamline and manage automation processes effectively.
Acquire practical skills in data manipulation, from variable management to CSV/Excel and data table conversions, empowering you to handle data-rich tasks with confidence.
Develop competence in managing controls and exploring various plugins and extensions, providing a broader toolkit for handling diverse automation projects.
Get hands-on experience with exception handling, debugging, logging, code management, and bot deployment, fundamental skills that ensure your automated processes are reliable and efficient.
How to deploy and control Bots with UiPath Orchestrator
The Hands-on Natural Language Processing (NLP) Boot Camp is an immersive, three-day course that serves as your guide to building machines that can read and interpret human language. NLP is a unique interdisciplinary field, blending computational linguistics with artificial intelligence to help machines understand, interpret, and generate human language. In an increasingly data-driven world, NLP skills provide a competitive edge, enabling the development of sophisticated projects such as voice assistants, text analyzers, chatbots, and so much more. Our comprehensive curriculum covers a broad spectrum of NLP topics. Beginning with an introduction to NLP and feature extraction, the course moves to the hands-on development of text classifiers, exploration of web scraping and APIs, before delving into topic modeling, vector representations, text manipulation, and sentiment analysis. Half of your time is dedicated to hands-on labs, where you'll experience the practical application of your knowledge, from creating pipelines and text classifiers to web scraping and analyzing sentiment. These labs serve as a microcosm of real-world scenarios, equipping you with the skills to efficiently process and analyze text data. Time permitting, you?ll also explore modern tools like Python libraries, the OpenAI GPT-3 API, and TensorFlow, using them in a series of engaging exercises. By the end of the course, you'll have a well-rounded understanding of NLP, and will leave equipped with the practical skills and insights that you can immediately put to use, helping your organization gain valuable insights from text data, streamline business processes, and improve user interactions with automated text-based systems. You?ll be able to process and analyze text data effectively, implement advanced text representations, apply machine learning algorithms for text data, and build simple chatbots.
What is Robotic Process Automation?
Scope and techniques of automation
Robotic process automation
About UiPath
The future of automation
Record and Play
UiPath stack
Downloading and installing UiPath Studio
Learning UiPath Studio
Task recorder
Step-by-step examples using the recorder
Sequence, Flowchart, and Control Flow
Sequencing the workflow Activities
Control flow, various types of loops, and decision making
Step-by-step example using Sequence and Flowchart
Step-by-step example using Sequence and Control flow
Data Manipulation
Variables and scope
Collections
Arguments ? Purpose and use
Data table usage with examples
Clipboard management
File operation with step-by-step example
CSV/Excel to data table and vice versa (with a step-by-step example)
Taking Control of the Controls
Finding and attaching windows
Finding the control
Techniques for waiting for a control Act on controls ? mouse and keyboard activities
Working with UiExplorer
Handling events
Revisit recorder
Screen Scraping
When to use OCR
Types of OCR available
How to use OCR
Avoiding typical failure points
Tame that Application with Plugins and Extensions
Terminal plugin
SAP automation
Java plugin
Citrix automation
Mail plugin
PDF plugin
Web integration
Excel and Word plugins
Credential management Extensions ? Java, Chrome, Firefox, and Silverlight
Handling User Events and Assistant Bots
What are assistant bots?
Monitoring system event triggers
Monitoring image and element triggers
Launching an assistant bot on a keyboard event
Exception Handling, Debugging, and Logging
Exception handling
Common exceptions and ways to handle them
Logging and taking screenshots
Debugging techniques
Collecting crash dumps
Error reporting
Managing and Maintaining the Code
Project organization
Nesting workflows
Reusability of workflows
Commenting techniques
State Machine
When to use Flowcharts, State Machines, or Sequences Using config files and examples of a config file
Integrating a TFS server
Deploying and Maintaining the Bot
Publishing using publish utility
Overview of Orchestration Server
Using Orchestration Server to control bots
Using Orchestration Server to deploy bots
License management
Publishing and managing updates

Robotic Surgery

Robotics

UiPath

UiPath Certified Advanced RPA Developer (UiARD)

RPA Boot Camp / Hands-On Robotic Process Automation (RPA) (TTAI4000)

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>This in an intermediate and beyond-level course is geared for experienced Python developers looking to delve into the exciting field of Natural Language Processing. It is ideally suited for roles such as data analysts, data scientists, machine learning engineers, or  anyone working with text data and seeking to extract valuable insights from it. If you're in a role where you're tasked with analyzing customer sentiment, building chatbots, or dealing with large volumes of text data, this course will provide you with practical, hands on skills that you can apply right away.</p>Overview<p>This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you'll:<br />Master the fundamentals of Natural Language Processing (NLP) and understand how it can help in making sense of text data for valuable insights.<br />Develop the ability to transform raw text into a structured format that machines can understand and analyze.<br />Discover how to collect data from the web and navigate through semi-structured data, opening up a wealth of data sources for your projects.<br />Learn how to implement sentiment analysis and topic modeling to extract meaning from text data and identify trends.<br />Gain proficiency in applying machine learning and deep learning techniques to text data for tasks such as classification and prediction.<br />Learn to analyze text sentiment, train emotion detectors, and interpret the results, providing a way to gauge public opinion or understand customer feedback.</p><p>The Hands-on Natural Language Processing (NLP) Boot Camp is an immersive, three-day course that serves as your guide to building machines that can read and interpret human language. NLP is a unique interdisciplinary field, blending computational linguistics with artificial intelligence to help machines understand, interpret, and generate human language. In an increasingly data-driven world, NLP skills provide a competitive edge, enabling the development of sophisticated projects such as voice assistants, text analyzers, chatbots, and so much more. Our comprehensive curriculum covers a broad spectrum of NLP topics. Beginning with an introduction to NLP and feature extraction, the course moves to the hands-on development of text classifiers, exploration of web scraping and APIs, before delving into topic modeling, vector representations, text manipulation, and sentiment analysis. Half of your time is dedicated to hands-on labs, where you'll experience the practical application of your knowledge, from creating pipelines and text classifiers to web scraping and analyzing sentiment. These labs serve as a microcosm of real-world scenarios, equipping you with the skills to efficiently process and analyze text data. Time permitting, you?ll also explore modern tools like Python libraries, the OpenAI GPT-3 API, and TensorFlow, using them in a series of engaging exercises. By the end of the course, you'll have a well-rounded understanding of NLP, and will leave equipped with the practical skills and insights that you can immediately put to use, helping your organization gain valuable insights from text data, streamline business processes, and improve user interactions with automated text-based systems. You?ll be able to process and analyze text data effectively, implement advanced text representations, apply machine learning algorithms for text data, and build simple chatbots.</p></div><div id="course-pills-2"><h4>Launch into the Universe of Natural Language Processing</h4><ul><li>The journey begins: Unravel the layers of NLP </li><li>Navigating through the history of NLP </li><li>Merging paths: Text Analytics and NLP </li><li>Decoding language: Word Sense Disambiguation and Sentence Boundary Detection </li><li>First steps towards an NLP Project</li></ul><h4>Unleashing the Power of Feature Extraction</h4><ul><li>Dive into the vast ocean of Data Types </li><li>Purification process: Cleaning Text Data </li><li>Excavating knowledge: Extracting features from Texts </li><li>Drawing connections: Finding Text Similarity through Feature Extraction </li></ul><h4>Engineer Your Text Classifier</h4><ul><li>The new era of Machine Learning and Supervised Learning </li><li>Architecting a Text Classifier </li><li>Constructing efficient workflows: Building Pipelines for NLP Projects </li><li>Ensuring continuity: Saving and Loading Models</li></ul><h4>Master the Art of Web Scraping and API Usage</h4><ul><li>Stepping into the digital world: Introduction to Web Scraping and APIs </li><li>The great heist: Collecting Data by Scraping Web Pages </li><li>Navigating through the maze of Semi-Structured Data</li></ul><h4>Unearth Hidden Themes with Topic Modeling</h4><ul><li>Embark on the path of Topic Discovery </li><li>Decoding algorithms: Understanding Topic-Modeling Algorithms </li><li>Dialing the right numbers: Key Input Parameters for LSA Topic Modeling </li><li>Tackling complexity with Hierarchical Dirichlet Process (HDP)</li></ul><h4>Delving Deep into Vector Representations</h4><ul><li>The Geometry of Language: Introduction to Vectors in NLP</li></ul><h4>Text Manipulation: Generation and Summarization</h4><ul><li>Playing the creator: Generating Text with Markov Chains </li><li>Distilling knowledge: Understanding Text Summarization and Key Input Parameters for TextRank </li><li>Peering into the future: Recent Developments in Text Generation and Summarization </li><li>Solving real-world problems: Addressing Challenges in Extractive Summarization</li></ul><h4>Riding the Wave of Sentiment Analysis</h4><ul><li>Unveiling emotions: Introduction to Sentiment Analysis Tools </li><li>Demystifying the Textblob library </li><li>Preparing the canvas: Understanding Data for Sentiment Analysis </li><li>Training your own emotion detectors: Building Sentiment Models</li></ul><h4>Optional: Capstone Project</h4><ul><li>Apply the skills learned throughout the course. </li><li>Define the problem and gather the data. </li><li>Conduct exploratory data analysis for text data. </li><li>Carry out preprocessing and feature extraction. </li><li>Select and train a model. ? Evaluate the model and interpret the results.</li></ul><h4>Bonus Chapter: Generative AI and NLP</h4><ul><li>Introduction to Generative AI and its role in NLP. </li><li>Overview of Generative Pretrained Transformer (GPT) models. </li><li>Using GPT models for text generation and completion. </li><li>Applying GPT models for improving autocomplete features. </li><li>Use cases of GPT in question answering systems and chatbots.</li></ul><h4>Bonus Chapter: Advanced Applications of NLP with GPT</h4><ul><li>Fine-tuning GPT models for specific NLP tasks. </li><li>Using GPT for sentiment analysis and text classification. </li><li>Role of GPT in Named Entity Recognition (NER). </li><li>Application of GPT in developing advanced chatbots. </li><li>Ethics and limitations of GPT and generative AI technologies.</li></ul></div>

Duration
3 Days
18 CPD hours
This course is intended for
This in an intermediate and beyond-level course is geared for experienced Python developers looking to delve into the exciting field of Natural Language Processing. It is ideally suited for roles such as data analysts, data scientists, machine learning engineers, or anyone working with text data and seeking to extract valuable insights from it. If you're in a role where you're tasked with analyzing customer sentiment, building chatbots, or dealing with large volumes of text data, this course will provide you with practical, hands on skills that you can apply right away.
Overview
This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you'll:
Master the fundamentals of Natural Language Processing (NLP) and understand how it can help in making sense of text data for valuable insights.
Develop the ability to transform raw text into a structured format that machines can understand and analyze.
Discover how to collect data from the web and navigate through semi-structured data, opening up a wealth of data sources for your projects.
Learn how to implement sentiment analysis and topic modeling to extract meaning from text data and identify trends.
Gain proficiency in applying machine learning and deep learning techniques to text data for tasks such as classification and prediction.
Learn to analyze text sentiment, train emotion detectors, and interpret the results, providing a way to gauge public opinion or understand customer feedback.
The Hands-on Natural Language Processing (NLP) Boot Camp is an immersive, three-day course that serves as your guide to building machines that can read and interpret human language. NLP is a unique interdisciplinary field, blending computational linguistics with artificial intelligence to help machines understand, interpret, and generate human language. In an increasingly data-driven world, NLP skills provide a competitive edge, enabling the development of sophisticated projects such as voice assistants, text analyzers, chatbots, and so much more. Our comprehensive curriculum covers a broad spectrum of NLP topics. Beginning with an introduction to NLP and feature extraction, the course moves to the hands-on development of text classifiers, exploration of web scraping and APIs, before delving into topic modeling, vector representations, text manipulation, and sentiment analysis. Half of your time is dedicated to hands-on labs, where you'll experience the practical application of your knowledge, from creating pipelines and text classifiers to web scraping and analyzing sentiment. These labs serve as a microcosm of real-world scenarios, equipping you with the skills to efficiently process and analyze text data. Time permitting, you?ll also explore modern tools like Python libraries, the OpenAI GPT-3 API, and TensorFlow, using them in a series of engaging exercises. By the end of the course, you'll have a well-rounded understanding of NLP, and will leave equipped with the practical skills and insights that you can immediately put to use, helping your organization gain valuable insights from text data, streamline business processes, and improve user interactions with automated text-based systems. You?ll be able to process and analyze text data effectively, implement advanced text representations, apply machine learning algorithms for text data, and build simple chatbots.
Launch into the Universe of Natural Language Processing
The journey begins: Unravel the layers of NLP
Navigating through the history of NLP
Merging paths: Text Analytics and NLP
Decoding language: Word Sense Disambiguation and Sentence Boundary Detection
First steps towards an NLP Project
Unleashing the Power of Feature Extraction
Dive into the vast ocean of Data Types
Purification process: Cleaning Text Data
Excavating knowledge: Extracting features from Texts
Drawing connections: Finding Text Similarity through Feature Extraction
Engineer Your Text Classifier
The new era of Machine Learning and Supervised Learning
Architecting a Text Classifier
Constructing efficient workflows: Building Pipelines for NLP Projects
Ensuring continuity: Saving and Loading Models
Master the Art of Web Scraping and API Usage
Stepping into the digital world: Introduction to Web Scraping and APIs
The great heist: Collecting Data by Scraping Web Pages
Navigating through the maze of Semi-Structured Data
Unearth Hidden Themes with Topic Modeling
Embark on the path of Topic Discovery
Decoding algorithms: Understanding Topic-Modeling Algorithms
Dialing the right numbers: Key Input Parameters for LSA Topic Modeling
Tackling complexity with Hierarchical Dirichlet Process (HDP)
Delving Deep into Vector Representations
The Geometry of Language: Introduction to Vectors in NLP
Text Manipulation: Generation and Summarization
Playing the creator: Generating Text with Markov Chains
Distilling knowledge: Understanding Text Summarization and Key Input Parameters for TextRank
Peering into the future: Recent Developments in Text Generation and Summarization
Solving real-world problems: Addressing Challenges in Extractive Summarization
Riding the Wave of Sentiment Analysis
Unveiling emotions: Introduction to Sentiment Analysis Tools
Demystifying the Textblob library
Preparing the canvas: Understanding Data for Sentiment Analysis
Training your own emotion detectors: Building Sentiment Models
Optional: Capstone Project
Apply the skills learned throughout the course.
Define the problem and gather the data.
Conduct exploratory data analysis for text data.
Carry out preprocessing and feature extraction.
Select and train a model. ? Evaluate the model and interpret the results.
Bonus Chapter: Generative AI and NLP
Introduction to Generative AI and its role in NLP.
Overview of Generative Pretrained Transformer (GPT) models.
Using GPT models for text generation and completion.
Applying GPT models for improving autocomplete features.
Use cases of GPT in question answering systems and chatbots.
Bonus Chapter: Advanced Applications of NLP with GPT
Fine-tuning GPT models for specific NLP tasks.
Using GPT for sentiment analysis and text classification.
Role of GPT in Named Entity Recognition (NER).
Application of GPT in developing advanced chatbots.
Ethics and limitations of GPT and generative AI technologies.

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