Free Books and Courses to learn Machine Learning
👇👇


Intro to Machine Learning Free Udacity Course 👇

https://imp.i115008.net/6bgOAb


Understanding Machine Learning: From Theory to Algorithms Free Book 👇

https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf


A Brief Introduction to Neural Networks 👇

http://www.dkriesel.com/en/science/neural_networks


Free course to Learn Machine Learning algorithms, softwares, deep learning 👇

https://bit.ly/3yW3fwJ


Machine Learning with PYTHON Free certified course 👇

https://www.freecodecamp.org/learn/machine-learning-with-python


#MachineLearning #Python #Course
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Debugging Machine Learning Models with Python: Develop high-performance, low-bias, and explainable machine learning and deep learning models, Ali Madani
2023 || #ENG

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Free Machine Learning Courses

Machine Learning with Python: Zero to GBMs
🎬 Watch hands-on coding-focused video tutorials
🧮 Practice coding with cloud Jupyter notebooks
💻 Build an end-to-end real-world course project
📜 Earn a verified certificate of accomplishment
📊 You will solve 2 coding assignments & build a course project where you'll train ML models using a large real-world datasets
🔗 Course Link

MIT RES.LL-005 Mathematics of Machine Learning and Big Data, IAP 2020
🎬 20 video lesson
Duration ⏰: 14 hours worth of material
🏃‍♂️ Self paced
Source: MIT open courseware
🔗 Course Link

Introduction to Machine Learning, IIT Kharagpur
🆓 Free Online Course
💻 44 Lecture Videos
🏃‍♂️ Self paced
Teacher 👨‍🏫 : Prof. S. Sarkar
🔗 Course Link

Machine Learning Crash Course
🧮 30+ Exercises
⏰ 15 hours
💻 Real-world case studies
📜 Lectures from Google researchers
📊 Interactive visualizations
🔗 Course Link

FOUNDATIONS OF MACHINE LEARNING
by Bloomberg
Understand the Concepts, Techniques and Mathematical Frameworks Used by Experts in Machine Learning
🎬 30 video lessons with slides
⏰ 28 hours
🔗 Course Link

Introduction to Machine Learning (Fall 2020)
By Massachusetts Institute of Technology, MIT
🎬 Video Lectures
⏰ Length: 13 weeks
🏃‍♂️ Self paced
Source: MIT
🔗 Course link

Introduction to Machine Learning Problem Framing
By Google
⏰ Length: 1 hour
🏃‍♂️ Self paced
Source: Google
🔗 Course link

Undergraduate Machine Learning (Nando de Freitas/University of British Columbia)
Author: prof Nando de Freitas
🎬 33 lessons
⏰ 21 hours
🏃‍♂️ Self paced
Source: YouTube
🔗 Course link

Stanford CS224W course on graph ML
A legendary Stanford CS224W course on graph ML now releases videos on YouTube for 2021
🎬 60 Videos
⏰ 30h
🔗 Course link

Books
Mathematics for Machine Learning
Approaching (Almost) Any Machine Learning Problem
Machine Learning

GitHub Repositories
Machine Learning University: Accelerated Natural Language Processing Class
Hands on ML notebook series
Machine learning cheat sheet with code

Article
Machine Learning is Fun!

#Machine_Learning
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Three different learning styles in machine learning algorithms:

1. Supervised Learning

Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.

A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.

Example problems are classification and regression.

Example algorithms include: Logistic Regression and the Back Propagation Neural Network.

2. Unsupervised Learning

Input data is not labeled and does not have a known result.

A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.

Example problems are clustering, dimensionality reduction and association rule learning.

Example algorithms include: the Apriori algorithm and K-Means.

3. Semi-Supervised Learning

Input data is a mixture of labeled and unlabelled examples.

There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.

Example problems are classification and regression.

Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.

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Microsoft is offering Machine Learning for Beginners Course for FREE!

If you are new to ML and looking for a course to start, check out this FREE course from Microsoft.

Course link 🔗🔗👇

https://microsoft.github.io/ML-For-Beginners/#/

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The "Approaching (Almost) Any Machine Learning Problem" book by 4x Kaggle grandmaster Abhishek Thakur is now available for free

https://github.com/abhishekkrthakur/approachingalmost/blob/master/AAAMLP.pdf

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Vodafone is hiring Data Engineer

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YouTube Playlists to Learn Machine Learning and Deep Learning.

YouTube Playlists by Krish Naik --->

Machine Learning
Deep Learning

YouTube Playlists by Codebasics --->

Machine Learning
Deep Learning

YouTube Playlists by Andrew Ng --->

Machine Learning
Deep Learning

YouTube Playlists by Sentdex --->

Machine Learning
Deep Learning

YouTube Playlists by Simplilearn --->

Machine Learning
Deep Learning

YouTube Playlists by edureka! --->

Machine Learning
Deep Learning

YouTube Playlists by FreeCodeCamp --->

ML+DL

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Top Machine Learning Question and Answer

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Agile Machine Learning: Effective Machine Learning Inspired by the Agile Manifesto (2019)

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