66 Azure Machine Learning courses delivered Online

Overview This comprehensive course on Machine Learning with Python will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Machine Learning with Python comes with accredited certification, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? There is no experience or previous qualifications required for enrolment on this Machine Learning with Python. It is available to all students, of all academic backgrounds. Requirements Our Machine Learning with Python is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Course Curriculum 4 sections • 21 lectures • 01:34:00 total length •Introduction to types of ML algorithm: 00:02:00 •SVM - Python Implementation: 00:06:00 •Introduction to types of ML algorithm: 00:02:00 •Importing a dataset in python: 00:02:00 •Resolving Missing Values: 00:06:00 •Managing Category Variables: 00:04:00 •Training and Testing Datasets: 00:07:00 •Normalizing Variables: 00:02:00 •Normalizing Variables - Python Code: 00:03:00 •Summary: 00:01:00 •Simple Linear Regression - How it works?: 00:04:00 •Simple Linear Regreesion - Python Implementation: 00:07:00 •Multiple Linear Regression - How it works?: 00:01:00 •Multiple Linear Regression - Python Implementation: 00:09:00 •Decision Trees - How it works?: 00:05:00 •Random Forest - How it works?: 00:03:00 •Decision Trees and Random Forest - Python Implementation: 00:04:00 •kNN - How it works?: 00:02:00 •kNN - Python Implementation: 00:10:00 •Decision Tree Classifier and Random Forest Classifier in Python: 00:10:00 •SVM - How it works?: 00:04:00

Discover the thrilling world of artificial intelligence with the 'Machine Learning Course with Python'. Immerse yourself in a voyage from foundational concepts, unveiling the mysteries behind algorithms, to diving deep into the cores of preprocessing, regression, and classification. Crafted meticulously, this course introduces Python as the catalyst, opening doors to data-driven decision-making and predictive analysis, empowering your journey in the ever-evolving field of machine learning. Learning Outcomes Grasp the foundational knowledge of various machine learning algorithms. Attain proficiency in preprocessing data for optimal outcomes. Master the nuances of regression analysis using Python. Delve into the intricacies of classification techniques. Enhance problem-solving abilities with practical Python-driven machine learning applications. Why choose this Machine Learning Course with Python course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments are designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Machine Learning Course with Python Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Who is this Machine Learning Course with Python course for? Aspiring data scientists eager to harness the power of machine learning. Python enthusiasts aiming to delve into its applications in AI. Professionals in the tech industry seeking a transition into data roles. Academics and researchers wanting to employ machine learning in their work. Business analysts aiming to leverage predictive analytics for better insights. Career path Data Scientist: £40,000 - £70,000 Machine Learning Engineer: £50,000 - £80,000 AI Researcher: £45,000 - £75,000 Data Analyst: £30,000 - £50,000 Python Developer: £35,000 - £65,000 Business Intelligence Developer: £40,000 - £60,000 Prerequisites This Machine Learning Course with Python does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Machine Learning Course with Python was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Module 01: Introduction to Machine Learning Algorithms Introduction to types of ML algorithm 00:02:00 Module 02: Preprocessing Importing a dataset in python 00:02:00 Resolving Missing Values 00:06:00 Managing Category Variables 00:04:00 Training and Testing Datasets 00:07:00 Normalizing Variables 00:02:00 Normalizing Variables - Python Code 00:03:00 Summary 00:01:00 Module 03: Regression Simple Linear Regression - How it works? 00:04:00 Simple Linear Regreesion - Python Implementation 00:07:00 Multiple Linear Regression - How it works? 00:01:00 Multiple Linear Regression - Python Implementation 00:09:00 Decision Trees - How it works? 00:05:00 Random Forest - How it works? 00:03:00 Decision Trees and Random Forest - Python Implementation 00:04:00 Module 04: Classification kNN - How it works? 00:02:00 kNN - Python Implementation 00:10:00 Decision Tree Classifier and Random Forest Classifier in Python 00:10:00 SVM - How it works? 00:04:00 SVM - Python Implementation 00:06:00 Assignment Assignment - Machine Learning Course with Python 00:00:00

Looking to bring your AI knowledge to life in a visually powerful way? Our Hands-on Machine Learning Project – Auto Image Captioning for Social Media Course takes you deep into the fascinating world of machine-generated descriptions. With social media driving more engagement through visuals than ever, this course helps you grasp how AI can generate captions that not only describe but also engage, contextualise, and communicate effectively. The focus is sharply tuned to image captioning technology and its role in today’s media-driven, attention-tight landscape. Expect to work your way through a structured learning journey where you’ll explore the building blocks of machine learning and how these elements are applied in automated image captioning. Whether you're aiming to polish your ML project portfolio or understand how AI is quietly shaping the way content is created and consumed online, this course gives you just the right amount of edge — no gimmicks, no fluff, just what matters. All content is delivered fully online, making it a flexible and accessible way to deepen your understanding of AI-powered communication tools used across social media platforms. Learning Outcomes: Develop an auto image captioning system using machine learning. Preprocess image and caption data. Define and evaluate the model. Deploy your machine learning app on AWS EC2 instance.Gain real-world experience in machine learning app development. The Hands-on Machine Learning Project - Auto Image Captioning for Social Media Platform course is designed to give you a comprehensive understanding of how to develop an auto image captioning system using machine learning. The course covers topics such as importing libraries, preprocessing text and image data, defining and evaluating the model, and deploying your machine learning app on AWS EC2 instance. You'll have access to the caption dataset and image dataset for training and test purposes, providing you with hands-on experience in machine learning app development. This course is perfect for aspiring data scientists, machine learning engineers, and developers who want to gain real-world experience in machine learning app development. With the skills and knowledge gained from this course, you'll be able to create your own auto image captioning system and start a career in the exciting field of machine learning. Hands on Machine Learning Project - Auto Image Captioning for Social Media Course Curriculum Section 01: Introduction Introduction to Course Section 02: Building the Auto Image Captioning Import the Libraries Accessing the Caption Dataset for Training Accessing the Image DataSet for Training Preprocessing the Text Data Pre-Process and Load Captions Data Loading the Captions for Training and Test Data Preprocessing of Image Data Loading Features for Train and Test Dataset Text Tokenization and Sequence Text Data Generators Define the Model Evaluation of Model Test the Model Section 03: Deployment of Machine Learning App Create Streamlit App Streamlit Prediction Test Streamlit App Deploy Streamlit on AWS EC2 Instance How is the course assessed? Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%. Exam & Retakes: It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable. Certification Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of __ GBP. £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery). CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Aspiring data scientists. Machine learning engineers. Developers interested in machine learning app development. Anyone interested in the field of machine learning. Professionals looking to upskill in the latest technology. Requirements There are no formal entry requirements for the course, with enrollment open to anyone! Career path Data Scientist: £40,000 to £80,000 per year. Machine Learning Engineer: £55,000 to £90,000 per year. Software Developer: £30,000 to £60,000 per year. Artificial Intelligence Developer: £40,000 to £80,000 per year. Computer Vision Engineer: £45,000 to £85,000 per year. Certificates Certificate of completion Digital certificate - £9 You can apply for a CPD Accredited PDF Certificate at the cost of £9. Certificate of completion Hard copy certificate - £15 Hard copy can be sent to you via post at the expense of £15.

🚀 Unlock the Power of Data with Our Machine Learning Course! 🤖 Are you ready to dive into the revolutionary world of Machine Learning? Welcome to our comprehensive course designed to equip you with the skills and knowledge needed to harness the potential of data-driven decision-making. 🎓 Machine Learning has rapidly emerged as one of the most transformative technologies of the 21st century. From powering intelligent virtual assistants to revolutionizing healthcare diagnostics, its applications are boundless. With our expertly crafted course, you'll embark on a journey that will demystify the complexities of Machine Learning and empower you to leverage its capabilities for diverse purposes. 💡 Why Machine Learning? In today's data-driven world, organizations across industries are seeking professionals who can extract actionable insights from vast amounts of data. Machine Learning offers the tools and techniques necessary to analyze complex datasets, identify patterns, and make predictions with unprecedented accuracy. By mastering Machine Learning, you'll gain a competitive edge in the job market and position yourself as a valuable asset to any organization. 📈 What You'll Learn: Our Machine Learning course covers a wide array of topics, including: Fundamentals of Machine Learning algorithms Supervised, unsupervised, and reinforcement learning techniques Data preprocessing and feature engineering Model evaluation and validation Deep learning and neural networks Practical applications and case studies With hands-on projects and real-world examples, you'll not only understand the theory behind Machine Learning but also gain practical experience in implementing algorithms and solving complex problems. Whether you're a beginner or an experienced data professional, our course is tailored to accommodate learners of all levels. 📊 Who is this for? Our Machine Learning course is ideal for: Aspiring data scientists and analysts Software engineers looking to transition into Machine Learning roles Business professionals seeking to leverage data for strategic decision-making Students and academics interested in exploring the forefront of technology No matter your background or experience level, our course provides a solid foundation in Machine Learning principles and techniques, setting you on the path to success in this rapidly evolving field. 🌟 Career Path: By mastering Machine Learning, you'll open doors to a myriad of exciting career opportunities, including: Data Scientist Machine Learning Engineer AI Researcher Business Intelligence Analyst Data Engineer With the demand for Machine Learning professionals on the rise, employers are actively seeking individuals with the skills and expertise to drive innovation and deliver impactful solutions. Whether you're looking to advance your current career or embark on a new professional journey, our course will equip you with the tools and knowledge needed to thrive in today's competitive job market. 💼 FAQ: Q: Is prior programming experience required to enroll in the course? A: While prior programming experience can be beneficial, our course is designed to accommodate learners of all backgrounds. We provide comprehensive tutorials and resources to help you grasp the fundamentals of programming and get started with Machine Learning. Q: How long does it take to complete the course? A: The duration of the course varies depending on your pace and level of commitment. On average, most learners complete the course within 3 to 6 months. However, you have the flexibility to study at your own pace and revisit materials as needed. Q: Are there any prerequisites for enrolling in the course? A: While there are no strict prerequisites, familiarity with basic mathematics, statistics, and programming concepts can be advantageous. We provide supplementary materials and support to help you build the necessary foundation for success in the course. Q: Will I receive a certificate upon completion of the course? A: Yes, upon successfully completing the course requirements, you'll receive a certificate of completion that validates your proficiency in Machine Learning concepts and techniques. This certificate can enhance your credentials and demonstrate your expertise to potential employers. Q: How does the course structure accommodate working professionals? A: Our course offers flexible scheduling options, allowing you to balance your studies with your professional and personal commitments. With on-demand access to course materials and resources, you can learn at your own convenience and progress at a pace that suits your lifestyle. Don't miss out on the opportunity to unlock your full potential with our Machine Learning course! Enroll today and embark on a transformative journey that will shape the future of your career. 🌐✨ Course Curriculum Module 1_ Introduction to Machine Learning Introduction to Machine Learning 00:00 Module 2_ Linear Regression Linear Regression 00:00 Module 3_ Logistic Regression Logistic Regression 00:00 Module 4_ Decision Trees and Random Forests Decision Trees and Random Forests 00:00 Module 5_ Support Vector Machines (SVMs) Support Vector Machines (SVMs) 00:00 Module 6_ k-Nearest Neighbors (k-NN) k-Nearest Neighbors (k-NN) 00:00 Module 7_ Naive Bayes Naive Bayes 00:00 Module 8_ Clustering Clustering 00:00 Module 9_ Dimensionality Reduction Dimensionality Reduction 00:00 Module 10_ Neural Networks Neural Networks 00:00

Duration 1 Days 6 CPD hours This course is intended for This introductory-level course is great for experienced technical professionals working in a wide range of industries, such as software development, data science, marketing and advertising, finance, healthcare, and more, who are looking to use the latest AI and machine learning techniques in their day to day. The hands-on labs in this course use Python, so you should have some familiarity with Python scripting basics. Overview Working in an interactive learning environment, led by our engaging OpenAI expert you'll: Understand the capabilities and products offered by OpenAI and how to access them through the OpenAI API. set up an OpenAI environment on Azure, including creating an Azure virtual machine and configuring the environment to connect to Azure resources. Gain hands-on experience building a GPT-3 based chatbot on Azure and implement advanced natural language processing capabilities. Use the OpenAI API to access GPT-3 and generate high-quality text Learn how to use Whisper to improve the quality of text generation. Understand the capabilities of DALL-E and use it to generate images for unique and engaging visuals. Geared for technical professionals, Quick Start to Azure AI Basics for Technical Users is a fun, fast paced course designed to quickly get you up to speed with OpenAI?s powerful tools and functionality, and to provide hands-on experience in setting up an OpenAI environment on Azure. Guided by our AI expert, you?ll explore the capabilities of OpenAI's GPT-3, Whisper and DALL-E, and build a chatbot on Azure. It will provide you with the knowledge and resources to continue your journey in AI and machine learning and have a good understanding of the potential of OpenAI and Azure for your projects. First, you?ll dive into the world of OpenAI, learning about its products and the capabilities they offer. You'll also discover how Azure's offerings for AI and machine learning can complement OpenAI's tools and resources, providing you with a powerful combination for your projects. And don't worry if you're new to Azure, we'll walk you through the process of setting up an account and creating a resource group. As you progress through the course, you'll get the chance to work with OpenAI's GPT-3, one of the most advanced large language models available today. You'll learn how to use the OpenAI API to access GPT-3 and discover how to use it to generate high-quality text quickly and easily. And that's not all, you'll also learn how to build a GPT-3 based chatbot on Azure, giving you the opportunity to implement advanced natural language processing capabilities in your chatbot projects. The course will also cover OpenAI Whisper, an OpenAI tool that can improve the quality of text generation, allowing you to create more coherent and natural language content. And you will learn about OpenAI DALL-E, an OpenAI tool that can generate images, giving you the ability to create unique and engaging visuals to enhance your content and projects. Introduction to OpenAI and Azure Explore OpenAI and its products, as well as Azure's offerings for AI and Machine Learning, allowing you to understand the tools and resources available to you for your AI projects. Explore OpenAI and its products Explore Azure and its offerings for AI and Machine Learning Get Hands-On: Setting up an OpenAI environment on Azure Walk through the process of setting up an OpenAI environment on Azure, giving you the hands-on experience needed to start building your own projects using OpenAI and Azure. Create an Azure virtual machine and installing the OpenAI SDK Configure the OpenAI environment and connecting to Azure resources Explore OpenAI GPT-3 Learn about GPT-3, one of OpenAI's most powerful language models, and how to use it to generate high quality text, giving you the ability to create natural language content quickly and easily. Review GPT-3 and its capabilities Use the OpenAI API to access GPT-3 Get Hands-on: Building a GPT-3 based chatbot on Azure Learn how to build a GPT-3 based chatbot on Azure, giving you the opportunity to learn how to implement advanced natural language processing capabilities in your chatbot projects. Setup an Azure Function and creating a chatbot Integrate GPT-3 with the chatbot OpenAI Whisper Explore Whisper, an OpenAI tool that can improve the quality of text generation, allowing you to create more coherent and natural language content. Explore Whisper and its capabilities Use Whisper to improve the quality of text generation OpenAI DALL-E Explore DALL-E, an OpenAI tool that can generate images, giving you the ability to create unique and engaging visuals to enhance your content and projects. Explore DALL-E and its capabilities Use the OpenAI API to access DALL-E What?s Next: Keep Going! Other ways OpenAI can impact your day to day Explore great places to check for expanded tools and add-ons for Azure OpenAI Where to go for help and support Quick Look at Generative AI and its Business Implications Understanding Generative AI Generative AI in Business Ethical considerations of Generative AI

If you aim to enhance your Machine Learning & Artificial Intelligence skills, our comprehensive Machine Learning & Artificial Intelligence course is perfect for you. Designed for success, this Machine Learning & Artificial Intelligence course covers everything from basics to advanced topics in Machine Learning & Artificial Intelligence. Each lesson in this Machine Learning & Artificial Intelligence course is crafted for easy understanding, enabling you to become proficient in Machine Learning & Artificial Intelligence. Whether you are a beginner or looking to sharpen your existing skills, this Machine Learning & Artificial Intelligence is the ideal choice. With our Machine Learning & Artificial Intelligence exclusive bundle, you will get a PDF Certificate, PDF Transcript and Digital Student ID Card (worth £50) Absolutely FREE. Courses are Included in This Machine Learning & Artificial Intelligence Bundle: Course 01: Machine Learning Basics Course 02: Hands-on Machine Learning Project - Auto Image Captioning for Social Media Course 03: Machine Learning with Python Course Course 04: Project on Deep Learning - Artificial Neural Network Course 05: Data Science & Machine Learning with R Training Course 06: ChatGPT for Marketing and Productivity with AI Tools Learning Outcomes of this Machine Learning & Artificial Intelligence Understand the foundational concepts of machine learning and its applications. Gain practical experience through hands-on machine learning projects and tools. Master Python programming for effective machine learning applications and tasks. Develop deep learning models using artificial neural networks effectively. Apply data science techniques using R for comprehensive data analysis. Learn to utilise AI tools for marketing and enhancing productivity in business. Why Choose Our Course? FREE Machine Learning & Artificial Intelligence certificate accredited Get a free student ID card with Machine Learning & Artificial Intelligence Training Get instant access to this Machine Learning & Artificial Intelligence course. Learn Machine Learning & Artificial Intelligence from anywhere in the world Machine Learning & Artificial Intelligence is affordable and simple to understand This bundle is an entirely online, interactive lesson with voiceover audio Lifetime access to the Machine Learning & Artificial Intelligence course materials The Machine Learning & Artificial Intelligence comes with 24/7 tutor support So enrol now in this Machine Learning & Artificial Intelligence Today to advance your career! Start your learning journey straightaway with Machine Learning & Artificial Intelligence! This Machine Learning & Artificial Intelligence's curriculum has been designed by Machine Learning & Artificial Intelligence experts with years of Machine Learning & Artificial Intelligence experience behind them. The Machine Learning & Artificial Intelligence course is extremely dynamic and well-paced to help you understand Machine Learning & Artificial Intelligence with ease. You'll discover how to master Machine Learning & Artificial Intelligence skills while exploring relevant and essential topics. *** Course Curriculum *** Course 01: Machine Learning Basics Section 01: Introduction Section 02: Regression Section 03: Predictors Section 04: Minitab Section 05: Regression Trees Section 06: Binary Logistics Regression Section 07: Classification Trees Section 08: Data Cleaning Section 09: Data Models Section 10: Learning Success Course 02: Hands-on Machine Learning Project - Auto Image Captioning for Social Media Section 01: Introduction Section 02: Building the Auto Image Captioning Section 03: Deployment of Machine Learning App Assessment Process of Machine Learning & Artificial Intelligence Once you have completed all the courses in the Machine Learning & Artificial Intelligence bundle, you can assess your skills and knowledge with an optional assignment. Our expert trainers will assess your assignment and give you feedback afterwards. CPD 60 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This Machine Learning & Artificial Intelligence bundle is suitable for everyone. Requirements You will not need any prior background or expertise in this Machine Learning & Artificial Intelligence. Career path This Machine Learning & Artificial Intelligence bundle will allow you to kickstart or take your career in the related sector to the next stage. Certificates CPD Accredited Digital certificate Digital certificate - Included CPD Accredited Hard copy certificate Hard copy certificate - £29 If you are an international student, you will be required to pay an additional fee of 10 GBP for international delivery, and 4.99 GBP for delivery within the UK, for each certificate

Duration 1 Days 6 CPD hours This course is intended for Software Engineers Overview The objective of this course is to learn the key language concepts to machine learning, Spark MLlib, and Spark ML. This course will teach you the key language concepts to machine learning, Spark MLlib, and Spark ML. The course includes coverage of collaborative filtering, clustering, classification, algorithms, and data volume. This course will teach you the key language concepts to machine learning, Spark MLlib, and Spark ML. The course includes coverage of collaborative filtering, clustering, classification, algorithms, and data volume.

Duration 3 Days 18 CPD hours This course is intended for This course is geared for experienced Scala developers who are new to the world of machine learning and are eager to expand their skillset. Professionals such as data engineers, data scientists, and software engineers who want to harness the power of machine learning in their Scala-based projects will greatly benefit from attending. Additionally, team leads and technical managers who oversee Scala development projects and want to integrate machine learning capabilities into their workflows can gain valuable insights from this course Overview Working in a hands-on learning environment led by our expert instructor you'll: Grasp the fundamentals of machine learning and its various categories, empowering you to make informed decisions about which techniques to apply in different situations. Master the use of Scala-specific tools and libraries, such as Breeze, Saddle, and DeepLearning.scala, allowing you to efficiently process, analyze, and visualize data for machine learning projects. Develop a strong understanding of supervised and unsupervised learning algorithms, enabling you to confidently choose the right approach for your data and effectively build predictive models Gain hands-on experience with neural networks and deep learning, equipping you with the know-how to create advanced applications in areas like natural language processing and image recognition. Explore the world of generative AI and learn how to utilize GPT-Scala for creative text generation tasks, broadening your skill set and making you a more versatile developer. Conquer the realm of scalable machine learning with Scala, learning the secrets to tackling large-scale data processing and analysis challenges with ease. Sharpen your skills in model evaluation, validation, and optimization, ensuring that your machine learning models perform reliably and effectively in any situation. Machine Learning Essentials for Scala Developers is a three-day course designed to provide a solid introduction to the world of machine learning using the Scala language. Throughout the hands-on course, you?ll explore a range of machine learning algorithms and techniques, from supervised and unsupervised learning to neural networks and deep learning, all specifically crafted for Scala developers. Our expert trainer will guide you through real-world, focused hands-on labs designed to help you apply the knowledge you gain in real-world scenarios, giving you the confidence to tackle machine learning challenges in your own projects. You'll dive into innovative tools and libraries such as Breeze, Saddle, DeepLearning.scala, GPT-Scala (and Generative AI with Scala), and TensorFlow-Scala. These cutting-edge resources will enable you to build and deploy machine learning models for a wide range of projects, including data analysis, natural language processing, image recognition and more. Upon completing this course, you'll have the skills required to tackle complex projects and confidently develop intelligent applications. You?ll be able to drive business outcomes, optimize processes, and contribute to innovative projects that leverage the power of data-driven insights and predictions. Introduction to Machine Learning and Scala Learning Outcome: Understand the fundamentals of machine learning and Scala's role in this domain. What is Machine Learning? Machine Learning with Scala: Advantages and Use Cases Supervised Learning in Scala Learn the basics of supervised learning and how to apply it using Scala. Supervised Learning: Regression and Classification Linear Regression in Scala Logistic Regression in Scala Unsupervised Learning in Scala Understand unsupervised learning and how to apply it using Scala. Unsupervised Learning:Clustering and Dimensionality Reduction K-means Clustering in Scala Principal Component Analysis in Scala Neural Networks and Deep Learning in Scala Learning Outcome: Learn the basics of neural networks and deep learning with a focus on implementing them in Scala. Introduction to Neural Networks Feedforward Neural Networks in Scala Deep Learning and Convolutional Neural Networks Introduction to Generative AI and GPT in Scala Gain a basic understanding of generative AI and GPT, and how to utilize GPT-Scala for natural language tasks. Generative AI: Overview and Use Cases Introduction to GPT (Generative Pre-trained Transformer) GPT-Scala: A Library for GPT in Scala Reinforcement Learning in Scala Understand the basics of reinforcement learning and its implementation in Scala. Introduction to Reinforcement Learning Q-learning and Value Iteration Reinforcement Learning with Scala Time Series Analysis using Scala Learn time series analysis techniques and how to apply them in Scala. Introduction to Time Series Analysis Autoregressive Integrated Moving Average (ARIMA) Models Time Series Analysis in Scala Natural Language Processing (NLP) with Scala Gain an understanding of natural language processing techniques and their application in Scala. Introduction to NLP: Techniques and Applications Text Processing and Feature Extraction NLP Libraries and Tools for Scala Image Processing and Computer Vision with Scala Learn image processing techniques and computer vision concepts with a focus on implementing them in Scala. Introduction to Image Processing and Computer Vision Feature Extraction and Image Classification Image Processing Libraries for Scala Model Evaluation and Validation Understand the importance of model evaluation and validation, and how to apply these concepts using Scala. Model Evaluation Metrics Cross-Validation Techniques Model Selection and Tuning in Scala Scalable Machine Learning with Scala Learn how to handle large-scale machine learning problems using Scala. Challenges of Large-Scale Machine Learning Data Partitioning and Parallelization Distributed Machine Learning with Scala Machine Learning Deployment and Production Understand the process of deploying machine learning models into production using Scala. Deployment Challenges and Best Practices Model Serialization and Deserialization Monitoring and Updating Models in Production Ensemble Learning Techniques in Scala Discover ensemble learning techniques and their implementation in Scala. Introduction to Ensemble Learning Bagging and Boosting Techniques Implementing Ensemble Models in Scala Feature Engineering for Machine Learning in Scala Learn advanced feature engineering techniques to improve machine learning model performance in Scala. Importance of Feature Engineering in Machine Learning Feature Scaling and Normalization Techniques Handling Missing Data and Categorical Features Advanced Optimization Techniques for Machine Learning Understand advanced optimization techniques for machine learning models and their application in Scala. Gradient Descent and Variants Regularization Techniques (L1 and L2) Hyperparameter Tuning Strategies

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.

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Developers responsible for developing Deep Learning applications Developers who want to understand concepts behind Deep Learning and how to implement a Deep Learning solution on AWS Overview This course is designed to teach you how to: Define machine learning (ML) and deep learning Identify the concepts in a deep learning ecosystem Use Amazon SageMaker and the MXNet programming framework for deep learning workloads Fit AWS solutions for deep learning deployments In this course, you?ll learn about AWS?s deep learning solutions, including scenarios where deep learning makes sense and how deep learning works. You?ll learn how to run deep learning models on the cloud using Amazon SageMaker and the MXNet framework. You?ll also learn to deploy your deep learning models using services like AWS Lambda while designing intelligent systems on AWS. Module 1: Machine learning overview A brief history of AI, ML, and DL The business importance of ML Common challenges in ML Different types of ML problems and tasks AI on AWS Module 2: Introduction to deep learning Introduction to DL The DL concepts A summary of how to train DL models on AWS Introduction to Amazon SageMaker Hands-on lab: Spinning up an Amazon SageMaker notebook instance and running a multi-layer perceptron neural network model Module 3: Introduction to Apache MXNet The motivation for and benefits of using MXNet and Gluon Important terms and APIs used in MXNet Convolutional neural networks (CNN) architecture Hands-on lab: Training a CNN on a CIFAR-10 dataset Module 4: ML and DL architectures on AWS AWS services for deploying DL models (AWS Lambda, AWS IoT Greengrass, Amazon ECS, AWS Elastic Beanstalk) Introduction to AWS AI services that are based on DL (Amazon Polly, Amazon Lex, Amazon Rekognition) Hands-on lab: Deploying a trained model for prediction on AWS Lambda Additional course details: Nexus Humans Deep Learning on AWS 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 Deep Learning on AWS 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.