Supervised Machine Learning: Regression and Classification (training)

Colleagues, in the “Supervised Machine Learning: Regression and Classification” program you will build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance).

Skill based training modules include: 1) Introduction to Machine Learning, 2) Regression with multiple input variables, and 3) Classification. You will again expertise with Regression Analysis, Algorithms, Logistic Regression, Feature Engineering, Supervised Learning, Model Training, Predictive Modeling, Artificial Intelligence, Data Preprocessing, Machine Learning Algorithms, Applied Machine Learning, Model Optimization, Model Evaluation, and Machine Learning. Key tools you will learn are Scikit Learn (Machine Learning Library), Classification Algorithms, Python Programming, NumPy, and Jupyter Notebooks.

Enroll today - teams and executives are welcome: https://imp.i384100.net/555bxN 

Recommended Reading:

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” (Audible) (Kindle)

4 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)

Much success in your Cyber-AI career, AI Academy (please subscribe and share with you colleagues)