Machine Learning with PyTorch (Nanodegree)

Colleagues, the Machine Learning with PyTorch program you will learn foundational machine learning techniques -- from data manipulation to unsupervised and supervised algorithms. Skill-based training modules include: 1) Supervised Learning - Naive bayes classifiers • Model evaluation • Support vector machines • Decision trees • Convolutions • scikit-learn • Perceptron • Categorical data visualization • Statistical modeling fundamentals • Chart types • Quantitative data visualization • Linear regression • Spam detection • Logistic regression • Professional presentations • Hyperparameter tuning, 2) Introduction to Neural Networks with PyTorch - Gradient descent • AI algorithms in Python • Training neural networks • NumPy • Backpropagation • Overfitting prevention • Deep learning fluency • PyTorch, 3) Unsupervised Learning - Gaussian mixture models • Single linkage clustering • K-means clustering • Dimensionality reduction • Audience segmentation • Cluster models • Principal component analysis • Independent component analysis • Density-based spatial clustering of applications with noise, 4) Introduction to Machine Learning and setup your computer with Python 3 using Anaconda, as well as setting up a text editor, 5) Supervised Learning - learn about different types of supervised learning and how to use them to solve real-world problems - Before diving into the many algorithms of machine learning, it is important to take a step back and understand the big picture associated with the entire field, Linear Regression - Linear regression is one of the most fundamental algorithms in machine learning. In this lesson, learn how linear regression works, Perceptron Algorithm - an algorithm for classifying data. It is the building block of neural networks, 6) Decision Trees - a structure for decision-making where each decision leads to a set of consequences or additional decisions, Naive Bayesian Algorithms are powerful tools for creating classifiers for incoming labeled data. Specifically Naive Bayes is frequently used with text data and classification problems, Support Vector Machines, Support vector machines are a common method used for classification problems. Ensemble Methods, Bagging and boosting are two common ensemble methods for combining simple algorithms to make more advanced models that work better than the simple algorithms would on their own, Model Evaluation Metrics, Learn the main metrics to evaluate models, such as accuracy, precision, recall, and Training and Tuning - Learn the main types of errors that can occur during training, and several methods to deal with them and optimize your machine learning models. You've covered a wide variety of methods for performing supervised learning -- now it's time to put those into action, and 6) Introduction to Neural Networks with PyTorch - Learn the fundamentals of neural networks with Python and PyTorch, and then use your new skills to create your own image classifier.

Enroll today (teams & executives are welcome): https://tinyurl.com/2kdsm5xh 

Download your free AI-ML-DL - Career Transformation Guide.

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)

2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) (Kindle)

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Kindle) or (Audible - coming soon!)

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

Deep Reinforcement Learning

Colleagues, this Deep Reinforcement Learning training program will equip you with high-demand and highly marketable skills to advance your AI career.  Learn the deep reinforcement learning skills that are powering amazing advances in AI. Then start applying these to applications like video games and robotics. Training modules include: 1) Introduction to Deep Reinforcement Learning - Get Help with Your Account, Learning Plan and helpful resources to accelerate your learning in this first part of the Nanodegree program, 2) Introduction to RL - Reinforcement learning is a type of machine learning where the machine or software agent learns how to maximize its performance at a task - the RL Framework: The Problem, Learn how to mathematically formulate tasks as Markov Decision Processes, The RL Framework: The Solution In reinforcement learning, agents learn to prioritize different decisions based on the rewards and punishments associated with different outcomes, Monte Carlo Methods, Write your own implementation of Monte Carlo control to teach an agent to play Blackjack!, Temporal-Difference Methods - Learn about how to apply temporal-difference methods such as SARSA, Q-Learning, and Expected SARSA to solve both episodic and continuing tasks, 3) Solve OpenAI Gym's Taxi-v2 Task - explore a mini project using OpenAI Gym!, RL in Continuous Spaces, Learn how to adapt traditional algorithms to work with continuous spaces, and 4) What's Next? - learn all about how to use neural networks as powerful function approximators in reinforcement learning - Value Based Methods, Policy-Based Methods, Multi-Agent Reinforcement Learning. Plus optional courses in Special Topics in Deep Reinforcement Learning, Neural Networks in PyTorch, Computing Resources, and C++ Programming. 

Enroll today (teams & executives are welcome): https://tinyurl.com/yyzkk7zj 

Download your free AI-ML-DL - Career Transformation Guide.

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) or (Kindle) 

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Kindle) or (Audible - coming soon!)

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

Machine Learning DevOps Engineer (training)

Colleagues, the Machine Learning DevOps Engineer training program will equip you to streamline the integration of machine-learning models and deploy them to a production environment. Acquire core skills in Clean Code Principles, Building a Reproducible Model Workflow, Deploying a Scalable ML Pipeline in Production plus ML Model Scoring and Monitoring. Training modules include: 1) Introduction to Machine Learning DevOps Engineer - 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. Then you will further expand your git and Github skills to work with teams. 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, 2) Clean Code Principles, 3) Building a Reproducible Model Workflow - become more efficient, effective, and productive in modern, real-world ML projects by adopting best practices around reproducible workflows. Learn 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. It also touches on Kubernetes, Kubeflow, and Great Expectations and how they relate to the content of the class, 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). Continuous Integration and Continuous Deployment is also covered which will be accomplished using GitHub Actions and Heroku, respectively. Finally, students will learn how to write a fast, type-checked, and auto-documented API using FastAPI, and 4) ML Model Scoring and Monitoring - automate the devops processes required to score and re-deploy ML models. Students will automate model training and deployment. Students will learn to diagnose operational issues with models, including data integrity and stability problems, timing problems, and dependency issues, and learn to set up automated reporting with API’s.

Enroll today (teams & executives are welcome): https://tinyurl.com/4358bm9v 

Download your free AI-ML-DL - Career Transformation Guide.

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) https://tinyurl.com/mae9ku3b or (Kindle) https://tinyurl.com/27jux34w 

2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” audiobook on Audible (https://tinyurl.com/bdfrtyj2) or ebook on Kindle (https://tinyurl.com/jfntsyj2) 

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Kindle) https://tinyurl.com/4bmmad9k  (Audible - coming soon!)

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

“ChatGPT … ChatGPT, Bard and Beyond” (new audio & ebook)

Colleagues, the new book “ChatGPT … ChatGPT, Bard and Beyond” (Audible) (Kindle) explores how generative conversational AI has the potential to improve accessibility for people with disabilities and those who struggle with language barriers, as AI models can be trained to understand and respond to a wide range of languages and dialects. Listen or read this new book now on Amazon. Generative conversational AI represents a major shift in how we interact with technology and has the potential to improve many aspects of our lives, from customer service and support to healthcare and accessibility. ChatGPT is a specific implementation of Generative conversational AI technology developed by OpenAI. It is a large language model trained on vast text data, allowing it to generate human-like responses to text inputs. In the context of conversational AI, ChatGPT can be used to build chatbots, virtual assistants, and other applications that require the ability to generate text in real time. The model's size and training data allow it to develop highly relevant and human-like text, making it well-suited for various applications. As a state-of-the-art Generative conversational AI, ChatGPT is the perfect tool for organizations looking to step up their communication game. Whether you want to improve interactions with customers, employees, or other stakeholders, ChatGPT makes it easy. Want to see just how much you can achieve with this powerful tool? Key topics include: 1) ChatGPT History and Development, 2) The Technology Underlying ChatGPT, 3) Applications of ChatGPT in Natural Language Processing and Generation, 4) Using ChatGPT for Language Translation and Summarization, 5) ChatGPT in Dialogue Systems and Conversational AI, 6) Ethics and Limitations of ChatGPT, 7) Future Developments and Advancements in ChatGPT, 8) The Impact of ChatGPT on Society, and 9) Conclusions and Next Steps.

Access the Transformative Innovation book series on Amazon today!

 

1 - “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) (Kindle) 

2 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Kindle) (Audible - coming soon!)

3 - “The Race for Quantum Computing”  (Audible) (Kindle) 

Regards, Genesys Digital (Amazon Author Page)

Become an AI Product Manager (Nanodegree Program)

Colleagues, in the “AI Product Manager” (Nanodegree Program) you will learn to develop AI products that deliver business value while building  skills that help you compete in the new AI-powered world. Learn how to evaluate the business value of an AI product. You’ll start by building familiarity and fluency with common AI concepts. You’ll then learn how to scope and build a data set, train a model, and evaluate its business impact. Finally, you’ll learn how to ensure a product is successful by focusing on scalability, potential biases, and compliance. Along the way, you’ll review case studies and examples to help you focus on how to define metrics to measure the business value for a proposed product. Training modules and hands-on projects involve: 1) Introduction to AI in Business - gain foundational knowledge of AI and machine learning, how to develop a business case for an AI application, and how and when to use AI in a product. A high-quality training data set is essential for machine learning models. Learn how to create a high-quality dataset, including how well the data fits a particular use case (Project: Create a Medical Image Annotation Data Set with Appen), 2) Building a Model - understand how neural networks produce a decision and how “training” works. You’ll also learn how to use training data and how to evaluate the results of a model (Project: Build a Model with Google AutoML), and 3) Measuring Impact and Updating Models - learn how to measure post-deployment impact, and how to make data-informed improvements on your model. You’ll also learn how to avoid unwanted bias, ensure security and compliance, and how to scale your product (Capstone Project).

Enroll today (teams & executives are welcome): https://tinyurl.com/3u8n666x 

Download your free AI-ML-DL - Career Transformation Guide.

Listen to or read our related AI books on Amazon”

• ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity (Kindle) (Audible - coming soon!)

• ChatGPT - The Era of Generative Conversational AI Has Begun (Audible) (Kindle) 

• AI Software Engineer: ChatGPT, Bard & Beyond (Audible) (Kindle) 

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

Become a Natural Language Processing Expert

AI Colleagues, become a “Natural Language Processing” expert by mastering the skills to get computers to understand, process, and manipulate human language. Build models on real data, and get hands-on experience with sentiment analysis, machine translation (3 months to complete). Learn cutting-edge natural language processing techniques to process speech and analyze text. Build probabilistic and deep learning models, such as hidden Markov models and recurrent neural networks. Training modules include: 1) Introduction to Natural Language Processing - learn text processing fundamentals, including stemming and lemmatization. Explore machine learning methods in sentiment analysis. Build a speech tagging model (Project: Part of Speech Tagging), 2) Computing with Natural Language - learn advanced techniques like word embeddings, deep learning attention, and more. Build a machine translation model using recurrent neural network architectures (Project: Machine Translation); and 3) Communicating with Natural Language - learn voice user interface techniques that turn speech into text and vice versa. Build a speech recognition model using deep neural networks (Project: Speech Recognizer).

Enroll today (teams & executives are welcome): https://tinyurl.com/8bc8asms 

Download your free AI-ML-DL - Career Transformation Guide.

Listen to the ““ChatGPT - The Era of Generative Conversational AI Has Begun” audiobook on Audible. (https://tinyurl.com/bdfrtyj2) or 

Read the ebook today on Amazon Kindle (https://tinyurl.com/jfntsyj2) 

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