Yacine Mahdid Courses

Learning Machine Learning Efficiently

1 review

Discover an efficient approach to learning machine learning, exploring the landscape, optimal paths, and practical strategies for mastering this complex field.

• YouTube
• 27 minutes
• On-Demand
• Free Video

AI Path Finding - Easy Search - Raw Problem Solving

Raw problem-solving session tackling an AI path-finding challenge using the Understand, Solve, Code framework, with real-time debugging and solution implementation.

• YouTube
• 52 minutes
• On-Demand
• Free Video

Shannon Entropy from Theory to Python

Learn to quantify information in sequences using Shannon Entropy. Explore the theory, formula, and Python implementation of this powerful information theory metric for data analysis and comparison.

• YouTube
• 23 minutes
• On-Demand
• Free Video

Kolmogorov Complexity for DNA Sequences Analysis in Python

Learn to analyze DNA sequence complexity using Kolmogorov Complexity and Python. Covers theoretical background, Lempel-Ziv algorithm, and practical implementation with code walkthrough and visualization.

• YouTube
• 36 minutes
• On-Demand
• Free Video

Distance Metrics Explained and Visualized in Python

Explore five common distance metrics in Python, including theory, code, and visualizations. Learn how to choose and apply the right metric for better results in data analysis and machine learning.

• YouTube
• 21 minutes
• On-Demand
• Free Video

How to Read Math as a Software Engineer

Learn to interpret mathematical formulas as a software engineer, reducing anxiety and enhancing coding skills through practical examples and step-by-step guidance.

• YouTube
• 22 minutes
• On-Demand
• Free Video

How to Easily Implement Any Data Structures

Learn effective techniques for implementing data structures with C++ examples, improving your understanding and skills in algorithms and data structures.

• YouTube
• 28 minutes
• On-Demand
• Free Video

Orthogonalized Minimum Spanning Tree and Their Application for Human Brain Mapping - Paper Review

Explore Orthogonalized Minimum Spanning Trees for brain connectivity analysis. Learn about data-driven thresholding methods, their application in human brain mapping, and practical implementation through code examples.

• YouTube
• 28 minutes
• On-Demand
• Free Video

Neural Style Transfer - From Theory to Pytorch Implementation

Explore neural style transfer using PyTorch, from theory to implementation. Learn to manipulate photos with deep neural networks, creating stunning AI-generated artwork.

• YouTube
• 51 minutes
• On-Demand
• Free Video

Img2Vec and UMAP for High-Dimensional Data Visualization

Learn to visualize high-dimensional image data using Img2Vec for embeddings, UMAP for dimensionality reduction, and Bokeh for interactive exploration. Ideal for vision classification projects.

• YouTube
• 50 minutes
• On-Demand
• Free Video

Stochastic Depth for Neural Networks - Implementation and Analysis

Explore stochastic depth in neural networks: a regularization method for residual networks that enhances training speed and test performance. Includes methodology explanation and PyTorch implementation.

• YouTube
• 27 minutes
• On-Demand
• Free Video

Solving Boggle Using AI - Dynamic Programming + Trie in Python

Learn to build an AI algorithm using dynamic programming and trie data structure to efficiently solve Boggle puzzles in Python, covering key concepts in graph theory and algorithm design.

• YouTube
• 36 minutes
• On-Demand
• Free Video

Intersection over Union (IoU) Explained with PyTorch

Learn to implement Intersection over Union (IoU) for object recognition using PyTorch. Covers theory, formulas, and practical code for bounding boxes and segmentation masks.

• YouTube
• 18 minutes
• On-Demand
• Free Video

Machine Learning with EEG Time-Series - Easy Python Project - Part 0

Learn to classify EEG data between awake and asleep states using Python and random forest. Covers data loading, preprocessing, feature engineering, and machine learning implementation.

• YouTube
• 23 minutes
• On-Demand
• Free Video

In-Context Learning in Deep Learning - How It Works in Large and Small Language Models

Explore in-context learning in LLMs: definition, significance, Bayesian framework, and implementation. Gain insights into this surprising capability of language models and its implications for AI.

• YouTube
• 12 minutes
• On-Demand
• Free Video