Free Python for AI/ML Learning Path

A practical guide to data wrangling using Python and the pandas library. The article covers reading data, accessing columns and rows, handling missing values, and data normalization, which are crucial steps for preparing data for analysis and machine learning.

This book provides a practical introduction to exploratory data analysis using Python. It covers topics such as distributions, probability, and hypothesis testing. The book is very hands-on and includes many case studies.

This free course covers the fundamentals of regression analysis, including linear regression, logistic regression, and other advanced techniques. It also provides hands-on coding experience in Python.

This course, part of a Machine Learning for Supply Chain Fundamentals specialization, explores all aspects of time series for demand prediction. It covers basic concepts like stationarity, trend, and seasonality, and then moves to autoregressive models and a final project on predicting demand using ARIMA in Python.

This beginner-level course introduces the exciting field of Computer Vision and its applications in various industries. You will learn about computer vision, its applications, and how to process images using Python, Watson AI, and OpenCV. The course also covers building image classification models and custom classifiers.

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 program covers the fundamentals of cybersecurity, including identifying threats, securing networks, and using tools like Python, Bash, and Linux. It also includes training on AI in cybersecurity from Google experts. No prior experience is required.

A comprehensive 7-course series that takes you from LLM business strategy to production deployment. You will learn to evaluate LLM opportunities, fine-tune models, and build production-ready applications using tools like Hugging Face and Python.

A tutorial that covers the essentials of synthetic data generation, including various techniques and tools. It provides practical Python code examples for creating synthetic data for AI and machine learning.

This course from Harvard University explores the concepts and algorithms at the foundation of modern artificial intelligence. It delves into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. The course covers topics including graph search algorithms, reinforcement learning, and neural networks.

This edX course focuses on the fundamentals of supervised machine learning, including both classification and regression. You will learn to apply various algorithms to real-life problems using Python and Scikit-Learn. The curriculum covers classification techniques and important concepts for evaluating and tuning your models.

University of Michigan Applied Data Science with Python

Python for Data Science Essential Training

OpenCV Python Tutorial 1 - Introduction & Images

What is OpenCV? - Python Beginners Tutorial 1

OpenCV Python Tutorial For Beginners 24 - Motion Detection and Tracking Using OpenCV Contours

Learn What is YOLO algorithm? | Deep Learning Tutorial 31 (TensorFlow, Keras & Python)

Introduction to Data Science with Python

Data Science with Python Certification

Complete PyTorch tutorial from basics to advanced topics. Learn tensors, autograd, neural networks, CNNs, RNNs, and more.