AI Academy

Friday, August 4, 2023

Artificial Intelligence-Machine Learning-Deep Learning - Career Transformation Guide (2023 V1) (Week #6 - article series: “Certification & Training - Deep Learning”)

Colleagues, our excerpt this week (#6) examines “Certification & Training - Deep Learning” in the global AI arena. The new Artificial Intelligence-Machine Learning-Deep Learning - Career Transformation Guide includes valuable information that enables you to accelerate your career growth and income potential - Career opportunities, Salaries (demand and growth), Certifications and Training programs, Publications and Portals along with Professional Forums and Communities.

Deep Learning

Build Basic Generative Adversarial Networks (GANs): In this course, you will: - Learn about GANs and their applications - Understand the intuition behind the fundamental components of GANs - Explore and implement multiple GAN architectures - Build conditional GANs capable of generating examples from determined categories The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more. Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs. This Specialization provides an accessible pathway for all levels of learners looking to break into the GANs space or apply GANs to their own projects, even without prior familiarity with advanced math and machine learning research. {DeepLearning.AI}

Deep Learning with TensorFlow & PyTorch: Deep Learning and Artificial Intelligence, TensorFlow Playground, weight initialization, unstable gradients, batch normalization, Convolutional Neural Networks, Keras, PyTorch. {Inform IT}

Deep Learning with Tensorflow, Keras and PyTorch: Intuitive, application-focused introduction to deep learning and TensorFlow, Keras, and PyTorch Overview Deep Learning with TensorFlow, Keras, and PyTorch LiveLessons is an introduction to deep learning that brings the revolutionary machine-learning approach to life with interactive demos from the most popular deep learning library, TensorFlow, and its high-level API, Keras, as well as the hot new library PyTorch. (InformIT)

Image Recognition with a Convolutional Neural Network: Image recognition is used in a wide variety of ways in our daily lives. This course will teach you how to tune and implement convolutional neural networks in order to implement image recognition and classification on a business case. In this course, Implement Image Recognition with a Convolutional Neural Network, you’ll understand how to implement image recognition and classification on your very own dataset. First, you’ll be introduced to the problem and dataset. Then, you’ll learn how to explore and prepare the dataset for the next step. Next, you’ll see how to build, train, and test a neural network on the dataset. Finally, you’ll explore how image augmentation and transfer learning help to lift the performance metrics involved in your solution. When you’re finished with this course, you’ll have the knowledge required to implement image recognition on any dataset of your choice. Training modules address: 1) Exploring and Preparing a Dataset for Image Recognition - What Are We Trying to Solve? Demos: Setting up Your Environment, Organizing the Dataset, Exploring the Dataset, Preprocessing and Preparing the Dataset, 2) Training a Convolutional Neural Network to Classify Images - What Are Convolutional Neural Networks? CNN: Convolutions, Activation, Pooling, Classification, Creating the CNN Architecture, Training the Model, Performance Metrics – How Well Did Your Model Do?, 3) Improving Performance of the Convolutional Neural Network - Better Performance – When and How?, Procuring Additional Training Data – Image Augmentation, Hyperparameter Tuning, Overfitting and Underfitting, Demo: Image Augmentation and Hyperparameter Tuning, What Is Transfer Learning?, and Transfer Learning – When and How?, and Demo: Improving Performance through Transfer Learning. {Pluralsight}

Introduction to Deep Learning: Demystify the models that underpin the recent AI revolution and provide a solid foundation for further learning. Skill-based training modules include: 1) Fundamentals, 2) Perceptron: Weights, Biases, Activation Functions, 3) Multi-neuron Networks: XOR and nonlinearity, and 4) Learning: Gradient Descent. After taking this course you will understand What deep learning is and how it differs from other types of machine learning and artificial intelligence, How deep learning models use neural networks to make computations, What types of problems deep learning models can be used to solve, Types of data needed to train deep learning models, Variety of inputs deep learning models receive and solutions they produce, Advantages that deep learning can offer over traditional machine learning, Why multi-neuron networks are able to solve complex problems, How neural networks use gradient descent and back-propagation to learn to make predictions. (Experfy)

Machine Vision, GANs, and Deep Reinforcement Learning: Modern machine vision involves automated systems outperforming humans on image recognition, object detection, and image segmentation tasks. Generative Adversarial Networks cast two Deep Learning networks against each other in a “forger-detective” relationship, enabling the fabrication of stunning, photorealistic images with flexible, user-specifiable elements. Deep Reinforcement Learning has produced equally surprising advances, including the bulk of the most widely-publicized “artificial intelligence” breakthroughs. Deep RL involves training an “agent” to become adept in given “environments,” enabling algorithms to meet or surpass human-level performance on a diverse range of complex challenges, including Atari video games, the board game Go, and subtle hand-manipulation tasks. Throughout these lessons, essential theory is brought to life with intuitive explanations and interactive, hands-on Jupyter notebook demos. Examples feature Python and straightforward Keras layers in TensorFlow 2, the most popular Deep Learning library. (InformIT)

Neural Networks and Deep Learning: Study the foundational concept of neural networks and deep learning. By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI. {DeepLearning.AI}

Probabilistic Deep Learning with TensorFlow 2: This is an increasingly important area of deep learning that aims to quantify the noise and uncertainty that is often present in real world datasets. This is a crucial aspect when using deep learning models in applications such as autonomous vehicles or medical diagnoses; we need the model to know what it doesn't know. You will learn how to develop probabilistic models with TensorFlow, making particular use of the TensorFlow Probability library, which is designed to make it easy to combine probabilistic models with deep learning. As such, this course can also be viewed as an introduction to the TensorFlow Probability library. You will learn how probability distributions can be represented and incorporated into deep learning models in TensorFlow, including Bayesian neural networks, normalizing flows and variational autoencoders. You will learn how to develop models for uncertainty quantification, as well as generative models that can create new samples similar to those in the dataset, such as images of celebrity faces. You will put concepts that you learn about into practice straight away in practical, hands-on coding tutorials, which you will be guided through by a graduate teaching assistant. In addition there is a series of automatically graded programming assignments for you to consolidate your skills. At the end of the course, you will bring many of the concepts together in a Capstone Project, where you will develop a variational autoencoder algorithm to produce a generative model of a synthetic image dataset that you will create yourself. This course follows on from the previous two courses in the specialization, Getting Started with TensorFlow 2 and Customizing Your Models with TensorFlow 2. The additional prerequisite knowledge required in order to be successful in this course is a solid foundation in probability and statistics. In particular, it is assumed that you are familiar with standard probability distributions, probability density functions, and concepts such as maximum likelihood estimation, change of variables formula for random variables, and the evidence lower bound (ELBO) used in variational inference. {Imperial College London}

Apply Generative Adversarial Networks (GANs): Explore the applications of GANs and examine them wrt data augmentation, privacy, and anonymity - Leverage the image-to-image translation framework and identify applications to modalities beyond images - Implement Pix2Pix, a paired image-to-image translation GAN, to adapt satellite images into map routes (and vice versa) - Compare paired image-to-image translation to unpaired image-to-image translation and identify how their key difference necessitates different GAN architectures - Implement CycleGAN, an unpaired image-to-image translation model, to adapt horses to zebras (and vice versa) with two GANs in one The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more. Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs. This Specialization provides an accessible pathway for all levels of learners looking to break into the GANs space or apply GANs to their own projects, even without prior familiarity with advanced math and machine learning research.

Generative Deep Learning with TensorFlow: Learn neural style transfer using transfer learning: extract the content of an image (eg. swan), and the style of a painting (eg. cubist or impressionist), and combine the content and style into a new image. b) Build simple AutoEncoders on the familiar MNIST dataset, and more complex deep and convolutional architectures on the Fashion MNIST dataset, understand the difference in results of the DNN and CNN AutoEncoder models, identify ways to de-noise noisy images, and build a CNN AutoEncoder using TensorFlow to output a clean image from a noisy one. c) Explore Variational AutoEncoders (VAEs) to generate entirely new data, and generate anime faces to compare them against reference images. d) Learn about GANs; their invention, properties, architecture, and how they vary from VAEs, understand the function of the generator and the discriminator within the model, the concept of 2 training phases and the role of introduced noise, and build your own GAN that can generate faces. The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture, and gives them the tools to create and train advanced ML models. This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models. (DeepLearning.AI}

Artificial Intelligence-Machine Learning-Deep Learning - Career Transformation Guide (2023 V1) (Week #5 - article series: “Certification & Training - Machine Learning”)

Colleagues, our excerpt this week (#4) examines “Certification & Training - Machine Learning” in the global AI arena. The new Artificial Intelligence-Machine Learning-Deep Learning - Career Transformation Guide includes valuable information that enables you to accelerate your career growth and income potential - Career opportunities, Salaries (demand and growth), Certifications and Training programs, Publications and Portals along with Professional Forums and Communities.

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}

Monday, July 31, 2023

Machine Learning DevOps Engineer (training)

Colleagues, time to up your game. The new “Machine Learning DevOps Engineer” training program will equip you to streamline the integration of machine-learning models and deploy them to a production-level environment. You will build the DevOps skills required to automate the various aspects and stages of machine learning model building and monitoring via four training modules - each with a hands-on project: 1) Clean Code Principles - Develop skills that are essential for deploying production machine learning models. First, you will put your coding best practices on auto-pilot by learning how to use PyLint and AutoPEP8. Finally, you will learn best practices associated with testing and logging used in production settings in order to ensure your models can stand the test of time (Project: Predict Customer Churn with Clean Code), 2) Building a Reproducible Model Workflow - Become more efficient, effective, and productive in modern, real-world ML projects by adopting best practices around reproducible workflows. In particular, it teaches the fundamentals of MLops and how to: a) create a clean, organized, reproducible, end-to-end machine learning pipeline from scratch using MLflow b) clean and validate the data using pytest c) track experiments, code, and results using GitHub and Weights & Biases d) select the best-performing model for production and e) deploy a model using MLflow (Project: Build an ML Pipeline for Short-term Rental Prices in NYC), 3) Deploying a Scalable ML Pipeline in Production - Deploy a machine learning model into production. En route to that goal students will learn how to put the finishing touches on a model by taking a fine grained approach to model performance, checking bias, and ultimately writing a model card. Students will also learn how to version control their data and models using Data Version Control (DVC). The last piece in preparation for deployment will be learning Continuous Integration and Continuous Deployment which will be accomplished using GitHub Actions and Heroku, respectively (Project: Deploying a Machine Learning Model on Heroku with FastAPI), and 4) Automated model scoring and monitoring - Automate the devops processes required to score and re-deploy ML models. Students will automate model training and deployment. They will set up regular scoring processes to be performed after model deployment, and also learn to reason carefully about model drift, and whether models need to be retrained and re-deployed. Students will learn to diagnose operational issues with models, including data integrity and stability problems, timing problems, and dependency issues. Finally, students will learn to set up automated reporting with API’s (Project: A Dynamic Risk Assessment System).

Enroll today (teams & execs are welcome): https://tinyurl.com/yc39fdst

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Much career success, Lawrence E. Wilson - Artificial Intelligence Academy (share with your team)