Intermediate Python for AI

But what is a neural network? | Deep learning chapter 1

The Essential Main Ideas of Neural Networks

Transformers Explained - How transformers work

Learn AI, Machine Learning, Deep Learning and Generative AI Explained

How AI Image Generators Work (Stable Diffusion / Dall-E) - Computerphile

Transformer Neural Networks - EXPLAINED! (Attention is all you need)

Illustrated Guide to Transformers Neural Network: A step by step explanation

Learn Machine Learning Like a GENIUS and Not Waste Time

All Machine Learning algorithms explained in 17 min

Natural Language Processing: Crash Course AI 7

This course teaches you how to analyze data using Python and popular libraries like Pandas and Num Py. You'll learn about data preparation, wrangling, and exploratory data analysis.

This course teaches how to use applied machine learning and text-mining techniques to analyze free-text data. You will learn to identify named entities, tag them with appropriate classifications, and develop multiple approaches from regular expressions to neural network models for extraction.

This University of London course provides a practical introduction to the K-Means clustering algorithm, with a focus on the underlying statistical concepts.

This course teaches how to build and deploy AI-powered web applications using Python and the Flask framework. It covers the end-to-end lifecycle, including creating AP Is, handling requests, and integrating IBM Watson AI libraries.

Learn to implement and evaluate Random Forest models for machine learning tasks using Python.

A project-based course where you will learn to build and evaluate classification trees using Python.

This specialization provides a deep dive into data wrangling techniques using Python, including data collection, assessment, and cleaning, as well as handling missing values.

This course focuses on A/B testing, a common application of hypothesis testing in the industry. You will learn how to design and analyze A/B tests using Python. The course covers topics such as sample size calculation, statistical power, and the interpretation of results.

A hands-on Data Camp course focused on applying causal inference techniques using Python libraries.

This free course teaches you the fundamentals of linear regression and its implementation in Python. It is a beginner-friendly course that covers the theory and practical aspects of this important machine learning algorithm.