AI/ML Fundamentals Track

Comprehensive ML course covering regression, classification, clustering, deep learning, NLP, reinforcement learning.

Text classification, sentiment analysis, topic modeling, text generation with spa Cy, NLTK.

This course delves into natural language processing (NLP), teaching you how to build models for tasks like sentiment analysis and text classification. You'll learn about logistic regression and naive Bayes, and how to represent text as vectors.

This course, part of the Deep Learning Specialization, focuses on convolutional neural networks (CNNs) and their application to computer vision tasks like image classification. You will learn to build and train CNNs and apply them to visual detection and recognition tasks.

This course from the University of Colorado Boulder provides a modern take on regression analysis using the R programming language. You will learn about various regression techniques and how to apply them to real-world data.

This course from Johns Hopkins University focuses on the application of multiple regression analysis in the field of public health. You will learn how to analyze and interpret data using regression models.

This is the first course in the Machine Learning Specialization. It provides a broad introduction to modern machine learning, including supervised learning (linear regression, logistic regression, neural networks, and decision trees). You will build machine learning models in Python using popular machine learning libraries Num Py and Scikit-Learn.

This professional certificate from Google builds on foundational data analytics skills, focusing on advanced topics like statistical analysis, machine learning, and predictive modeling using Python and Tableau. It includes hands-on projects to prepare learners for senior data analyst and junior data scientist roles.

A hands-on project where you'll train a Bidirectional Neural Network and LSTMs based deep learning model to detect fake news from a given news corpus. This is a practical skill for media companies to automatically predict the authenticity of news articles.

This specialization by Stanford University, taught by Andrew Ng, is a highly popular and comprehensive introduction to machine learning. It covers fundamental concepts including Support Vector Machines (SV Ms) and kernel methods. The course is designed for beginners and provides a strong theoretical and practical foundation.

This specialization is a great starting point for beginners who want to learn Python for data science. While not focused solely on regression, it provides the necessary programming foundation to tackle more advanced machine learning courses.

Complete Machine Learning & Data Science Bootcamp 2025

This course provides a comprehensive, hands-on introduction to machine learning on the Google Cloud Platform, with a specific focus on Vertex AI. Students will learn about various GCP services, including compute, storage, and databases, before diving into machine learning workflows. The curriculum covers building and deploying models using GCP's AutoML for tabular, image, and text data, as well as custom model training and deployment on the AI Platform and Vertex AI. The course is designed to equip learners with the practical skills needed to create and manage machine learning pipelines on Google Cloud.

Machine Learning for Business Analytics

Machine Learning and AI with Python Web Apps

Google Cloud Machine Learning Complete Course

This course covers Support Vector Machines (SVM) from basic to advanced kernel-based models. It is designed for those who want to apply machine learning to real-world business problems and includes topics like hyperparameter tuning and model performance evaluation.

This course provides a comprehensive understanding of the theory behind Support Vector Machines, including the derivation of Linear SVM, the Kernel SVM using Lagrangian Duality, and the application of Quadratic Programming. It covers practical applications like image recognition and spam detection.

Learn Support Vector Machines in R Studio, from basic SVM models to advanced kernel-based SVM models. This course is for those who want to apply machine learning to real-world business problems using the R programming language.

This course covers a wide range of machine learning algorithms in R, including a dedicated section on tree-based methods.