Supervised Machine Learning: Regression and Classification

Colleagues, the “Supervised Machine Learning: Regression and Classification” is part of Machine Learning Specialization from DeepLearning.AI. You will learn to build machine learning models in Python using popular machine learning libraries NumPy & scikit-learn. Also learn to build & train supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression. Acquire high-demand skills involving Linear Regression, Regularization to Avoid Overfitting, Logistic Regression for Classification, Gradient Descent, and Supervised Learning. 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, and more.). The curriculum includes: Week 1 - Introduction to Machine Learning: Applications of machine learning, What is machine learning?, Supervised learning, Unsupervised learning, Jupyter Notebooks, Linear regression model part, Cost function formula, Visualizing the cost function and examples, Gradient descent, Implementing gradient descent, Gradient descent intuition, Learning rates, Gradient descent for linear regression, and Running gradient descent; Week 2 - Regression with multiple input variables: Multiple features, Vectorization part, Gradient descent for multiple linear regression, Feature scaling part, Feature scaling part, Checking gradient descent for convergence, Choosing the learning rate, Feature engineering, and Polynomial regression; and Week 3 - Classification: Motivations, Logistic regression, Decision boundary, Cost function for logistic regression, Simplified Cost Function for Logistic Regression, Gradient Descent Implementation, The problem of overfitting, Addressing overfitting, Cost function with regularization, Regularized linear regression, and Human-Centered AI. 

Enroll today (teams & execs welcome): https://imp.i384100.net/555bxN 

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” (Audible) or (Kindle) 

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