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Advanced Machine Learning and Signal Processing: Access to valuable insights into Supervised and Unsupervised Machine Learning Models used by experts in many field relevant disciplines. We’ll learn about the fundamentals of Linear Algebra to understand how machine learning modes work. Then we introduce the most popular Machine Learning Frameworks for python: Scikit-Learn and SparkML. SparkML is making up the greatest portion of this course since scalability is key to address performance bottlenecks. We learn how to tune the models in parallel by evaluating hundreds of different parameter-combinations in parallel. We’ll continuously use a real-life example from IoT (Internet of Things), for exemplifying the different algorithms. For passing the course you are even required to create your own vibration sensor data using the accelerometer sensors in your smartphone. So you are actually working on a self-created, real dataset throughout the course. {IBM}
AWS Certified Machine Learning: AWS Machine Learning-Specialty (ML-S) Certification exam, AWS Exploratory Data Analysis covers topics including data visualization, descriptive statistics, and dimension reduction and includes information on relevant AWS services, Machine Learning Modeling. {Pearson IT}
AWS Machine Learning Engineer Learn the data science and machine learning skills required to build and deploy machine learning models in production using Amazon SageMaker. Training modules with hands-on projects cover: 1) Introduction to Machine LearningIn this course, you'll start learning about machine learning through high level concepts through AWS SageMaker. Create machine learning workflows, starting with data cleaning and feature engineering, to evaluation and hyperparameter tuning. Finally, you'll build new ML workflows with highly sophisticated models such as XGBoost and AutoGluon (Project: Bike Sharing Demand with AutoGluon), 2) Developing Your First ML Workflow - machine learning workflows on AWS. Learn how to monitor machine learning workflows with services like Model Monitor and Feature Store. With all this, you’ll have all the information you need to create an end-to-end machine learning pipeline (Project: Build and ML Workflow on SageMaker), 3) Deep Learning Topics within Computer Vision and NLP - train, finetune, and deploy deep learning models using Amazon SageMaker. Learn about advanced neural network architectures like Convolutional Neural Networks and BERT, as well as how to finetune them for specific tasks. Finally, you will learn about Amazon SageMaker and you will take everything you learned and do them in SageMaker Studio (Project: Image Classification using AWS SageMaker), 4) .Operationalizing Machine Learning Projects on SageMaker - deploy professional machine learning projects on SageMaker. It also covers security applications. Learn how to deploy projects that can handle high traffic and how to work with especially large datasets (Project: Operationalizing an AWS ML Project), and 5) Capstone Project - .Inventory Monitoring at Distribution Centers. To build this project, students will have to use AWS Sagemaker and good machine learning engineering practices to fetch data from a database, preprocess it and then train a machine learning model. This project will serve as a demonstration of end-to-end machine learning engineering skills that will be an important piece of their job-ready portfolio. (Udacity)
Build, Train, and Deploy Machine Learning Pipelines using BERT: Learn to automate a natural language processing task by building an end-to-end machine learning pipeline using Hugging Face’s highly-optimized implementation of the state-of-the-art BERT algorithm with Amazon SageMaker Pipelines. Your pipeline will first transform the dataset into BERT-readable features and store the features in the Amazon SageMaker Feature Store. It will then fine-tune a text classification model to the dataset using a Hugging Face pre-trained model, which has learned to understand the human language from millions of Wikipedia documents. Finally, your pipeline will evaluate the model’s accuracy and only deploy the model if the accuracy exceeds a given threshold. Practical data science is geared towards handling massive datasets that do not fit in your local hardware and could originate from multiple sources. One of the biggest benefits of developing and running data science projects in the cloud is the agility and elasticity that the cloud offers to scale up and out at a minimum cost. The Practical Data Science Specialization helps you develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker. This Specialization is designed for data-focused developers, scientists, and analysts familiar with the Python and SQL programming languages and want to learn how to build, train, and deploy scalable, end-to-end ML pipelines - both automated and human-in-the-loop - in the AWS cloud. {AWS & DeepLearning.AI}
Data Structures, Algorithms, and Machine Learning Optimization: Use "big O" notation to characterize the time efficiency and space efficiency of a given algorithm, enabling you to select or devise the most sensible approach for tackling a particular machine learning problem with the hardware resources available to you, get acquainted with the entire range of the most widely-used Python data structures, including list-, dictionary-, tree-, and graph-based structures, develop a working understanding of all of the essential algorithms for working with data, including those for searching, sorting, hashing, and traversing, discover how the statistical and machine learning approaches to optimization differ, and why you would select one or the other for a given problem you're solving, understand exactly how the extremely versatile (stochastic) gradient descent optimization algorithm works and how to apply it and learn "fancy" optimizers that are available for advanced machine learning approaches (e.g., deep learning) and when you should consider using them. Training modules include: 1) Data Structures and Algorithms, 2) "Big O" Notation, 3) List-Based Data Structures, 4) Searching and Sorting, 5) Sets and Hashing, 6) Trees, 7) Graphs, 8) Machine Learning Optimization, and 9) Fancy Deep Learning Optimizers. {InformIT}
Generating Code with ChatGPT API: This course walks learners through setting up their OpenAI trial, generating API keys, and making their first API request. Gain key skills including ChatGPT API, OpenAI API, Python Libraries, Python Programming and Generative AI API. It enables learners to set up their OpenAI trial, generating API keys, and making their first API request. Learners are introduced to the basics of using the ChatGPT-API to generate a variety of responses.Learners are introduced to the basics of using the ChatGPT-API to generate a variety of responses. Training modules will equip you in: 1) Introduction to ChatGPT-API, 2) Coding with ChatGPT-API, and 3) Practice with ChatGPT-API. {Coursera}
Getting Started with Generative AI APIs: This course walks learners through setting up their OpenAI trial, generating API keys, and making their first API request. Build high demand and highly marketable skills in ChatGPT API, OpenAI API, Python Libraries, Python Programming and Generative AI API. Learners are introduced to the basics of natural language generation using OpenAI GPT-3 before building a movie recommendation system. Build your subject-matter expertise. When you enroll in this course, you'll also be enrolled in this Specialization. Learn new concepts from industry experts, gain a foundational understanding of a subject or tool, develop job-relevant skills with hands-on projects, and earn a shareable career certificate The three training modules include: 1) Introduction to ChatGPT, 2) Large Language Models and 3( AI to API. {Cousera}
Linear Algebra for Machine Learning: Learn the role of algebra in machine and deep learning, understand the fundamentals of linear algebra, a ubiquitous approach for solving for unknowns within high-dimensional spaces, develop a geometric intuition of what's going on beneath the hood of machine learning algorithms, including those used for deep learning, be able to more intimately grasp the details of machine learning papers as well as all of the other subjects that underlie ML, including calculus, statistics, and optimization algorithms, manipulate tensors of all dimensionalities including scalars, vectors, and matrices, in all of the leading Python tensor libraries: NumPy, TensorFlow, and PyTorch, and reduce the dimensionality of complex spaces down to their most informative elements with techniques such as eigendecomposition (eigenvectors and eigenvalues), singular value decomposition, and principal components analysis. Training modules address: 1) Orientation to Linear Algebra, 2) Data Structures for Algebra, 3) Common Tensor Operations, 4) Solving Linear Systems, 5) Matrix Multiplication, 6) Special Matrices and Matrix Operations, 7) Eigenvectors and Eigenvalues, 8) Matrix Determinants and Decomposition, and 9) Machine Learning with Linear Algebra. {InformIT}
Machine Learning / AI Engineer: Learn to solve business challenges with machine learning systems and decision-making algorithms. About this Career Path: Build machine learning apps: Build machine learning models, then turn them into applications to enable better decision-making, Maximize your algorithms: Tune your machine learning models to minimize run time and maximize performance, Prepare for your career: Get job-ready with portfolio projects. The program is comprised of 7 units, 37 projects and 39 lessons: 1) Introduction to Machine Learning Engineer Career Path - Discover what you will learn on your journey to becoming a Machine Learning Engineer, 2) Machine Learning Fundamentals - this Skill Path will introduce you to the foundational algorithms and techniques, 3) Software Engineering for Machine Learning/AI Engineers - gain the skills to bridge the gap between machine learning and software engineering, and prepare to solve problems on an engineering team, 4) Intermediate Machine Learning - learn intermediate machine learning methods, 5) Building Machine Learning Pipelines - learn how to build machine pipelines, 6) Machine Learning/AI Engineer: Final Portfolio -show off your knowledge of machine learning engineering by developing your final portfolio project on a topic of your choice. {Codecademy}
Machine Learning with Python: from Linear Models to Deep Learning: Understand principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning, Implement and analyze models such as linear models, kernel machines, neural networks, and graphical models, Choose suitable models for different applications, Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering. Lectures address: 1) Linear classifiers, separability, perceptron algorithm, 2) Maximum margin hyperplane, loss, regularization, 3) Stochastic gradient descent, over-fitting, generalization, 4) Linear regression, 5) Recommender problems, collaborative filtering, 6) Non-linear classification, kernels, 7) Learning features, Neural networks, 8) Deep learning, back propagation, 9) Recurrent neural networks, 10) Generalization, complexity, VC-dimension, 11) Unsupervised learning: clustering, 11) Generative models, mixtures, 12) Mixtures and the EM algorithm, 13) Learning to control: Reinforcement learning, 14) Reinforcement learning continued, and 15) Applications: Natural Language Processing. Projects cover: 1) Automatic Review Analyzer, 2) Digit Recognition with Neural Networks, and 3) Reinforcement Learning. {EDx}
Machine Learning Engineering Career Track Program: Deploy ML Algorithms and build your own portfolio. More than 50% of the Springboard curriculum is focused on production engineering skills. In this course, you'll design a machine learning/deep learning system, build a prototype and deploy a running application that can be accessed via API or web service. The 500+ hour curriculum features a combination of videos, articles, hands-on projects, and career-related coursework. Skill-based training modules include: 1) Battle-Tested Machine Learning Models, 2) Deep Learning, 3) Computer Vision and Image Processing, 4) The Machine Learning Engineering Stack, 5) ML Models At Scale and In Production, 6) Deploying ML Systems to Production, and 7) Working With Data. You will build a realistic, complete, ML application that’s available to use via an API, a web service or, optionally, a website. One-on-one Mentorship provides you with weekly guided calls with your personal mentor, an industry expert. Our career coaching calls will help your mentor. Create a successful job search strategy, Build your Machine Learning Engineering network, Find the right job titles and companies, Craft a Machine Learning Engineer resume and LinkedIn profile, Ace the job interview and Negotiate your salary. (Springboard)
Machine Learning - Regression: Case Study - Predicting Housing Prices In the first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression. In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets. Learning Outcomes: By the end of this course, you will be able to: -Describe the input and output of a regression model. -Compare and contrast bias and variance when modeling data. -Estimate model parameters using optimization algorithms. -Tune parameters with cross validation. -Analyze the performance of the model. -Describe the notion of sparsity and how LASSO leads to sparse solutions. -Deploy methods to select between models. -Exploit the model to form predictions. -Build a regression model to predict prices using a housing dataset. -Implement these techniques in Python. {University of Washington}
Machine Learning with Mahout Certification: Machine Learning Fundamentals, Apache Mahout Basics, History of Mahout, Supervised and Unsupervised Learning techniques, Mahout and Hadoop, Introduction to Clustering, Classification, Hyperparameters and Pipelines. {Edureka}
Machine Learning with Python for Everyone (Parts 1-3): Turn introductory machine learning concepts into concrete code using Python, scikit-learn, and friends. Our focus is on stories, graphics, and code that build your understanding of machine learning; we minimize pure mathematics. You learn how to load and explore simple datasets; build, train, and perform basic learning evaluation for a few models; compare the resource usage of different models in code snippets and scripts; and briefly explore some of the software and mathematics behind these techniques. Part I - Software, Mathematics, Classification, Regression, Part II - Evaluating Learning Performance, Classifiers, Regressors, and Part III - Classification Methods, Regression Methods, Manual Feature Engineering, Hyperparameters and Pipelinesand. {InformIT}
Machine Learning with PyTorch: Open Source Torch Library - machine learning, and for deep learning specifically, are presented with an eye toward their comparison to PyTorch, scikit-learn library, similarity between PyTorch tensors and the arrays in NumPy or other vectorized numeric libraries,clustering with PyTorch, image classifiers. {Inform IT}
MLOps Tools: MLflow and Hugging Face: This course covers two of the most popular open source platforms for MLOps (Machine Learning Operations): MLflow and Hugging Face. We’ll go through the foundations on what it takes to get started in these platforms with basic model and dataset operations. You will start with MLflow using projects and models with its powerful tracking system and you will learn how to interact with these registered models from MLflow with full lifecycle examples. Then, you will explore Hugging Face repositories so that you can store datasets, models, and create live interactive demos. By the end of the course, you will be able to apply MLOps concepts like fine-tuning and deploying containerized models to the Cloud. This course is ideal for anyone looking to break into the field of MLOps or for experienced MLOps professionals who want to improve their programming skills. {Duke University}
Model Tuning for Machine Learning: Slingshot the predictive capabilities of your models, far out-pacing the limits of out-of-box ML. From a ground-up perspective, we'll understand how a model functions, the part of the model that is able to fit the data on its own, and how important additional tuning and fitting by a trained ML engineer is. The 32 training modules address: Introduction and expectation-setting, Hyperparameters, Intro to Bayesianism, Intro to Bayesian Model Averaging, Bayesian Model Averaging- Specification, Occam's Window, Computing the Integral, Bayesian Model Averaging-Worked Example, Intro to Bootstrap Aggregation, Intro to Bootstrap Aggregation- CART, Problem with Bagged Decision Trees, Random Forests- Start to Finish, Random Forests: Time-Accuracy Tradeoff, Boosted Trees- Differences from Random Forest, Boosted trees- Adaboost Procedure, XGBoost- Gradient Boosting, Boosted Trees- Final Decision, Introduction to Hyper-Parameters- Basics, Hyperparameters in Decision Trees, Hyperparamters in Decision Trees- Levels, Hyperparameters in decision trees- AUC, Finding optimal hyperparameters- Brute Force, Finding Optimal Hyperparameters- Sanity Check, Intro to Stacking, Intro to Stacking- Motivation, Stacking- Pedigree, Know Your Data, Time/Value Tradeoff, and Example Scenario - Network Transactions. (Experfy)
Supervised Learning - Linear Regression in Python: You will learn to apply Least Squares regression and its assumptions to real world data. Then we'll improve on that algorithm with Penalized Regression and non-parametric Kernel methods. Understanding basic statistical modeling and assumptions. Build & Evaluate supervised linear models using Least squares, Penalized least squares, Non-parametric methods, and Model selection and fit on real world applications. Training modules include: 1) Introduction to Supervised Linear Regression Course, 2) Introduction to Machine Learning and Supervised Regression - Introduction to Machine Learning and Supervised Regression, Discuss the overall AI ecosystem and how Machine Learning (ML) is part of that ecosystem. - Understand the 3 different types of algorithms that make up ML - Provide some intuition for why functions and optimizations are important in ML. - Differences between Statistical and ML approaches to supervised linear regression. (Quiz 1Module 2 - ML and Supervised Regression), 3) Machine Learning - Understanding Assumptions, Survey the statistical concepts important to understanding Linear Algorithms. - Design of experiments. - Conducting experiments. - Understand the difference between linear and non-linear functions (Quiz: Linear Regression Assumptions), 4) Least Squares Regression - Ordinary Regression - Least Squares Regression - Ordinary Regression. Develop the simple linear regression algorithm. Understand the basic linear regression assumptions. Learn to identify when assumption violations occur. Understand how to evaluate model output (Quiz - Simple Regression), 5) Least Squares Regression - Multiple Regression (Quiz - Multiple Regression), 6) Penalized Regression - L1/L2 Optimization (Quiz - Penalized Regression), 7) Kernel Methods - Support Vector Machines (Quiz - Support Vector Machines), 8) Kernel Methods - Gaussian Process Regression (Quiz - Gaussian Process Regression), 9) Summary and Real World Applications (Quiz - Case Study). {Experify}
Recommended Reading:
“AI Software Engineer” - The Interview Prodigy book series (Audible) (Kindle)
Download your free AI-ML-DL - Career Transformation Guide (2023 v1). [https://lnkd.in/gZNSGaEM]
New audio & ebook: “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) (Kindle)
Much career success, Lawrence E. Wilson - Artificial Intelligence Academy (share with your team)
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