152 Deep Learning courses

If you are working on data science projects and want to create powerful visualization and insights as an outcome of your projects or are working on machine learning projects and want to find patterns and insights from your data on your way to building models, then this course is for you. This course exclusively focuses on explaining how to build fantastic visualizations using Python. It covers more than 20 types of visualizations using the most popular Python visualization libraries, such as Matplotlib, Seaborn, and Bokeh along with data analytics that leads to building these visualizations so that the learners understand the flow of analysis to insights.

Do you want to build a simple, reliable, and error-free chatbot for your business? If yes, then this is the course for you! Learn to build a chatbot with Amazon Lex, a fully-controlled AI service with sophisticated natural language models to create, develop, test, and deploy chatbots (conversational interfaces) in applications.

Prepare to inspire and lead with Train the Trainer Level 5. Develop advanced skills to deliver impactful training and foster professional growth.

Start your data science journey with this carefully constructed comprehensive course and get hands-on experience with Python for data science. Gain in-depth knowledge about core Python and essential mathematical concepts in linear algebra, probability, and statistics. Complete data science training with 13+ hours of content.

Duration 5 Days 30 CPD hours This course is intended for The skills covered in this course converge on four areas-software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification Overview In this course, you will develop AI solutions for business problems. You will: Solve a given business problem using AI and ML. Prepare data for use in machine learning. Train, evaluate, and tune a machine learning model. Build linear regression models. Build forecasting models. Build classification models using logistic regression and k -nearest neighbor. Build clustering models. Build classification and regression models using decision trees and random forests. Build classification and regression models using support-vector machines (SVMs). Build artificial neural networks for deep learning. Put machine learning models into operation using automated processes. Maintain machine learning pipelines and models while they are in production Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions. Solving Business Problems Using AI and ML Topic A: Identify AI and ML Solutions for Business Problems Topic B: Formulate a Machine Learning Problem Topic C: Select Approaches to Machine Learning Preparing Data Topic A: Collect Data Topic B: Transform Data Topic C: Engineer Features Topic D: Work with Unstructured Data Training, Evaluating, and Tuning a Machine Learning Model Topic A: Train a Machine Learning Model Topic B: Evaluate and Tune a Machine Learning Model Building Linear Regression Models Topic A: Build Regression Models Using Linear Algebra Topic B: Build Regularized Linear Regression Models Topic C: Build Iterative Linear Regression Models Building Forecasting Models Topic A: Build Univariate Time Series Models Topic B: Build Multivariate Time Series Models Building Classification Models Using Logistic Regression and k-Nearest Neighbor Topic A: Train Binary Classification Models Using Logistic Regression Topic B: Train Binary Classification Models Using k-Nearest Neighbor Topic C: Train Multi-Class Classification Models Topic D: Evaluate Classification Models Topic E: Tune Classification Models Building Clustering Models Topic A: Build k-Means Clustering Models Topic B: Build Hierarchical Clustering Models Building Decision Trees and Random Forests Topic A: Build Decision Tree Models Topic B: Build Random Forest Models Building Support-Vector Machines Topic A: Build SVM Models for Classification Topic B: Build SVM Models for Regression Building Artificial Neural Networks Topic A: Build Multi-Layer Perceptrons (MLP) Topic B: Build Convolutional Neural Networks (CNN) Topic C: Build Recurrent Neural Networks (RNN) Operationalizing Machine Learning Models Topic A: Deploy Machine Learning Models Topic B: Automate the Machine Learning Process with MLOps Topic C: Integrate Models into Machine Learning Systems Maintaining Machine Learning Operations Topic A: Secure Machine Learning Pipelines Topic B: Maintain Models in Production

This course is about developing core skills that will stay with you for a lifetime. It is designed such that you can watch the material and follow along step-by-step. It focuses on the implementation of YOLOv4 to get you up and running. You'll be an object detecting ninja in no time and be able to graduate to more advanced content.

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.

This course is designed to explore creative potential and hone artistic skills using ChatGPT. It covers how to use ChatGPT, generate ideas, research for a novel, create comics, and use other AI tools. Additionally, the course introduces ChatGPT for storytelling by providing prompts and refining its output to generate story ideas and characters.

Duration 1 Days 6 CPD hours This course is intended for This course is designed for non-technical business executives who are tasked with making business decisions about emerging technologies in their businesses. Overview You will learn:Blockchain Cloud BasicsIoT OverviewMobility and Ambient ComputingMachine Learning and Deep LearningChatbots, Robotics, and More This course is designed for non-technical business executives looking to learn and understand emerging technologies. Blockchain Cloud BasicsIoT OverviewMobility and Ambient ComputingMachine Learning and Deep LearningChatbots, Robotics, and More

¡Hola, amigos!  On the 18th 19th and 20th of December (19:30 GMT) we will offer an online free conversation class.  20th December Levels C1 to C2.  We will meet other students, get to know each other and, of course, practice Spanish. Come and meet us! We will be learning some Navidad vocabulary, talks about Spanish and latino traditions and of course we will have lot of fun.  Book your place in our web!  Levels: A1 to C2. Check the web to know the available days for each level.