Machine Learning with Python for Everyone (Part 1)

Colleagues, the Machine Learning with Python for Everyone Part 1 Learning Foundations focus is on showing you how to 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; while minimizing 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. Learn how to Build and apply simple classification and regression models, Evaluate learning performance with train-test splits, Assess learning performance with metrics tailored to classification and regression, and measure the resource usage of your learning models. Skill-based training modules include: 1) Software Background - the environment used to run the code and several of the fundamental software packages used throughout the lessons. Mark discusses scikit-learn, seaborn, and pandas--high-level packages that have many powerful features. Mark also introduces numpy and matplotlib--more foundational packages, 2) Mathematical Background - mathematical ideas: probability, linear combinations, and geometry. He approaches these concepts from a practical and computational viewpoint. He introduces them but shies away from theory, 3) Beginning Classification (Part I) - focuses on building, training, and evaluating simple classification models. He starts by introducing you to a practice dataset. It also covers train-test splits, accuracy, and two models: k-nearest neighbors and naive Bayes, 4) Beginning Classification (Part II) - two ways to evaluate classifiers. He shows you how to evaluate learning performance with accuracy and how to evaluate resource utilization for memory and time, 5) Beginning Regression (Part I) - demonstrates building, training, and basic evaluation of simple regression models. He starts with a practice dataset. It discusses different ways of measuring the center of numerical data, and then he discusses two models: k-nearest neighbors and linear regression, and 6) Beginning Regression (Part II) - how to select good models from a basket of possible models. Then, it covers how to evaluate learning and resource consumption of regressors in notebook and standalone scenarios.

Enroll today (teams & executives are welcome): https://tinyurl.com/ya5rrrpf 

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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)