Path to become Data Scientist(Free Videos and reading resources)

Machine learning Introduction:

• ML Intro
• what is ML
• 7 steps in ML
• How to Enter a Kaggle Competition (using Kernels) | Kaggle

Algebra:

• Linear Albegra| chapter 3
• 3 blue and 1 brown

Probability and statistics:

• Probability and statistics in data science using python
  
• Khan Academy for probability
  
• Khan Academy for statistics
  
• coursera| Basic statistics
  

Python for data science

• Datacamp|python and plot

Machine Learning

• Machine Learning by Andrew Ng| concept
  
• statisticsfun|linear regression|concept
  
• Applied Machine Learning
• Machine Learning fun easy| concept
• Practical ML|sentdex|python
• ML| Udacity
• Machine Learning Recipes with Josh Gordon
• Google |mastering machine learning| Crash course| tensorflow

Evaluation Metrics

• ML error metrics
• datcamp|metrics
• AUC and ROC curve
• What's the difference between accuracy and precision?

Deep Learning

• Neural network|3Blue 1Brown
• Neural network | hugo
• Keras|edureka
• Keras|udemy
• Getting Started with Keras (AI Adventures)
• Ten steps to keras|
• Deeplearning.ai
  Course 1,2,3,4,5 of deep learning specialization
  
• Convolutional Neural Networks
  
• RNN
  
• DL|udemy
• DL|RNN
• 7 step to mastering deep learning
• Python for data science
  
• Transfer Learning
  
• Lecture 10: Recurrent Neural Networks, Image Captioning, LSTM
  
• MIT deep Learning
  

Natural Language Processing:

• Deep learning for NLP by Richard
  
• Deep Learning for NLP
• Intro to NLP with spaCy (1): Detecting programming languages | Episode 1: Data exploration
• Building new NLP solutions with spaCy and Prodigy - Matthew Honnibal
• Advanced NLP with Spacy
• A Code-First Introduction to Natural Language Processing

Recommedation engine

• coursera
• Andre ng| chapter 16
• How does Netflix recommend movies? Matrix Factorization
• Movie Recommendation System with Collaborative Filtering

Computer Vision

• opencv tutorials
• image-segmentation-tips-and-tricks-from-kaggle-competitions
• pyimagesearch| best

Big data

• big data analysis using spark
  

Reinforcement Learning

• Deep learning| reinforcement Learning
• Simplified Reinforcement learning
• TensorFlow and deep reinforcement learning, without a PhD (Google I/O '18)
• Reinforcement Learning Course
• code camp
• Edureka

Artificial Intelligent

• UC Berkeley
• UC Berkeley|materials

Data Scientist Interiew

• DataCamp |DataChats

Awesome talk On Machine Learning

• Andreas Mueller|MIT

Data Science Courses

• Learn Deep learning fundamental with Keras from IBM
• Python for data science
  
• Master the skills needed to solve complex data challenges
• Edx| Learn Data Science by doing data science

Data science competition

• kaggle
• drivendata
• crowdanalytix
• codalab
• tianchi

Data Scientist Interiew

• DataCamp |DataChats
• machine-learning-algorithms-interview

Data Science Courses

• Data science course
  
• Master the skills needed to solve complex data challenges
  
• Edx| Learn Data Science by doing data science
  

Data Science Blogs Resources

Blog courses

• Machine Learning Mastery
• Machine Learning is fun series
• Best kaggle kernel tutorials
• Data school
• Getting started with kaggle
• Machine Learning @ Berkeley|crash
• Kaggle
• ML from scratch
• Mlcourse.ai
• Andrew Trask
• Andrej karpathy

Visualization of model

• Tensorboard
• Hand-on Tensorboard
• Weight and Biases

Math for Data Sciecne

• math for ML
• Linear Algebra guide
• Linear Algebra cheatsheet
• A brief introduction to probability and statistics
• review probability
• Probability for ML
• probability ppt

Linear Regression

• ML cheatsheet| linear
• Simple Linear Regression Tutorial for Machine Learning
• regression

Logistic Regression

• ML cheatsheet| logistic
• Machine Learning Mastery| logistic

Naive Bayes

• Practical explanation naive bayes classifier
• understanding bayes theorem with ratios

K nearest Neighbour

• implementing your own knn

Support Vector Machines

• introduction to support vector machines svm
• standford notes
• standford linear classification

Decision trees

• Datacamp| decision trees

• How To Implement The Decision Tree Algorithm From Scratch In Python

• Decision tree |medium

• Ensemble

• Ensemble_learning algo

Time series

• great articles on Time series

Clustering

• Kmean visuallization
• DB scan visualization
• clustering
• 14 Clustering tutorials

Deep Learning

• 15 Deep Learning tutorials
• Initialization weight in NN
• Parameter optimization in neural networks

Tensorflow

• tensorflow|documentation
• TensorFlow: A primer
• RNNs in Tensorflow
• Implementing a CNN for Text Classification in TensorFlow
• how to run text summarization with tensorflow

Transfer Learning

• transfer

Keras

• keras documentaion
• keras|kdnuggets

NLP

• Coursera| NLP
• Standford NLP course
• Stanford deep NLP
• Data Schools|NLP
• OXFORD |DLNLP
• Washington
• The Definitive Guide to Natural Language Processing
• Approaching-almost-any-nlp-problem-on-kaggle
• NLP is fun
• Deep Learning for NLP
• Advanced NLP with Spacy
• A Code-First Introduction to Natural Language Processing
• practitioner guide to NLP

Chatbot

• Dialogflow
• webbook

Big Data

• big data 11

GAN

• All about GANs

Reinforcement Learning

• Learning Reinforcement Learning
• Intro to Reinforcement
• RL tutorials
• Deep Reinforcement Learning: Pong from Pixels

Machine Learning best practice

• ML rules
• which ML algo to use
• Data-Analysis-and-Machine-Learning-Projects

Open cv

• Pyimagesearch

AI Trend

• AI trend

Data Science Projects

• kaggle project with kernel

Datasets

• 50 NLP Datasets
• Data science Datasets
• Machine Mastery
• Cambridgespark
• Public Datasets
• NLP dataset

Books

• ISLR
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow]s by Aurélien Géron
• Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 by Sebastian Raschka