A list of Learning resouces for machine learning and deep learning. It consists of full learning plan and also have list of research papers, datasets, conference and talk and Projects.

• Learning Plan
• Books
• Other Free Courses
• Talks and Tutorials
• Benchmarks and Datasets
• ML-DL Projects
• Top Github Repositories
• Conferences
• Youtube Channels
• How to Contribute

• Learn Python - Full Course for Beginners
• Learn Python Programming Masterclass

• Python for Data Science and Machine Learning Bootcamp
• Python for Data Science - Course for Beginners - duration 12 hours
• Data Science With Python - duration 1 hour

• Data Visualization | Intellipaat
• Python Data Visualization Projects Examples | Simplilearn

• Mathematics for Machine Learning and data Science
• Essence of linear algebra

• SQL for data Analysis
• SQL for Data Science

• Mastering Data Analysis in Excel
• Data Analusis in excel

• Exploratory Data Analysis In Python

• Inferential Statistics

• Hypothesis Testing in Data Science

• EDA Projects
• EDA Projects- 40 examples

• Definition and Concepts
• Machine Learning vs Traditional Programming
• ML Applications

• Types of Machine learning

• Image and Speech Recognition
• Natural Language Processing
• Recommender Systems
• Fraud Detection
• Autonomous Vehicles
• Applications of Machine learning

• Data Collection and Cleaning
• Data Preprocessing
• Feature Selection and Engineering
• Model Selection and Training
• Evaluation and Fine-Tuning
• Build Your First Machine Learning Project

• Data Cleaning
• Data Sources and Formats
• Handling Missing Data
• Outlier Detection and Removal

• Data Transformation

• Normalization and Standardization and Scaling

• Log Transformation

• Binning and One-Hot Encoding

• Feature Construction
• Feature Selection

• Complete Case Analysis
• Handling missing numerical data
• Handling missing categorical data
• Missing indicator
• KNN Imputer
• MICE

• Standarization
• Normalization

• Data Cleaning

• Linear Regression
• Gradient Descent
• Logistic Regression
• Support Vector Machines
• Naive Bayes
• K Nearest Neighbors
• Decision Trees
• Random Forest
• Bagging
• Adaboost
• Gradient Boosting
• Xgboost
• Principle Component Analysis (PCA)
• KMeans Clustering
• Heirarchical Clustering
• DBSCAN
• T-sne

• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems- Aurélien Géron, O'Reilly Media, Inc.", 2022.

• Data Mining: Practical Machine Learning Tools and Techniques.
• Python Machine Learning

• The Hundred-Page Machine Learning Book
• Approaching (Almost) Any Machine Learning Problem
• Pattern Recognition and Machine Learning
• Python Data Science Handbook: Essential Tools for Working with Data
• Machine learning by tom mitchell

• Machine Learning Specialization by Andrew NG on Coursera
• Kaggle Courses
• Machine Learning Crash Course
• Machine Learning Course by EdX
• Machine Learning Specialization University of Washington on Coursera
• Introduction to Machine Learning Course by Udacity
• Machine Learning with Python by IBM

• Gradient Dissent - A Machine Learning Podcast by Weights & Biases
• This Week in ML & AI Podcast
• Data Skeptic
• Linear Digressions
• O'Reilly Data Show
• The Talking Machines
• Practical AI
• Lex Fridman Podcast
• Talk Python To Me

• Kaggle Dataset
• UCI Machine Learning Repository
• Google Dataset
• Microsoft Research Open Data
• VisualData Discovery
• AWS Registry of Open Data on AWS
• AwesomeData GitHub

• 500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
• Papers with Codes

• ML-For-Beginners by Microsoft
• ML-YouTube-Courses
• Mathematics For Machine Learning
• MIT Deep Learning Book
• Machine Learning ZoomCamp
• Machine Learning Tutorials
• Awesome Machine Learning
• Machine Learning cheatsheets
• Machine learning Interview
• Awesome Production Machine Learning

• List of ML AI conferences in 2023 and 2024
• List of ML AI journals

• StatQuest with Josh Starmer
• deeplearning.ai
• Google DeepMind
• Sentdex
• Data School
• Abhishek Thakur
• Machine Learning with Phil

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