Introduction to Machine Learning by Maxime Vandegar
Free Download Introduction to Machine Learning by Maxime Vandegar
Published 11/2024
Created by Maxime Vandegar
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 23 Lectures ( 4h 21m ) | Size: 1.61 GB
Build Machine Learning Algorithms from Scratch (No Sklearn Shortcuts!)
What you'll learn
Implement Machine Learning Algorithms from Scratch
Compare Custom Implementations to Sklearn
Understand the Math and Logic Behind ML
Gain Confidence in ML Fundamentals
Build and Train Neural Networks
Develop Practical Coding Skills
Requirements
Mathematics Knowledge: Familiarity with high school-level math, including basic algebra, metrics, and an understanding of derivatives.
Programming Skills: Proficiency in object-oriented programming (preferably Python) to follow and implement coding exercises effectively.
Description
Master the art of machine learning by implementing algorithms from scratch, step-by-step! In this hands-on course, you won't just learn the theory — you'll write the code behind popular machine learning techniques yourself. From linear regression and decision trees to advanced neural networks, every part of each algorithm will be built from the ground up. Then, we'll benchmark our implementations against industry-standard libraries like Scikit-learn, proving that our custom algorithms can match or even outperform them in efficiency.Why this course is different:Learn from an Expert: With a background in advanced academic research and real-world consulting, your instructor combines cutting-edge theory with practical insights.No Sklearn Black Box: Say goodbye to plug-and-play libraries. Learn how every piece of the machine learning puzzle fits together by coding each algorithm from scratch.Hands-On Coding: Every concept is accompanied by code implementations from scratch, ensuring you don't just learn — you create.Comprehensive Comparisons: Put your custom-built algorithms head-to-head with Sklearn's implementations to understand optimization, speed, and accuracy.Complete Neural Network Implementation: Build and train neural networks from scratch, understanding every layer, activation function, and backpropagation step.By the end of this course, you'll have a deep understanding of how machine learning algorithms work under the hood and the confidence to implement them in any project — no shortcuts, just pure mastery.
Who this course is for
Aspiring Data Scientists
Developers Transitioning to Machine Learning
Students and Researchers
Professionals in Applied Fields
Consultants and Entrepreneurs
Machine Learning Enthusiasts
Homepage
https://www.udemy.com/course/introduction-machine-learning/Rapidgator
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