MACHINE LEARNING/ AI
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Machine Learning ML - MIT
Prediction: ML And Statistics - MIT
Machine Learning (CS229) - Andrew Ng, Ron Dror/ Stanford University
Machine Learning Crash Course - UC Berkeley
Deep Learning (Book) - Ian Goodfellow, Yoshua Bengio and Aaron Courville
Foundations of Data Science (Book) - Avrim Blum, John Hopcroft, Ravi Kannan
Foundations of Data Science - Microsoft Videos Lectures
L-1: Foundations of Data Science
L-2: Foundations of Data Science
L-3: Foundations of Data Science
L-4: Foundations of Data Science
L-5: Foundations of Data Science: Length Squared Sampling in Matrices
L-6: Foundations of Data Science: Singular Value Decomposition – I
L-7: Foundations of Data Science: Singular Value Decomposition – II
L-8: Foundations of Data Science: Low Rank Approximation (LRA) via Length Squared Sampling
L-9: Foundations of Data Science: Two Applications of SVD
ML/ AI in Java
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Artificial Intelligence I: Basics And Games In Java - Udemy/ Holczer Balazs
Artificial Intelligence Ii: Neural Networks In Java- Udemy/ Holczer Balazs
Artificial Intelligence Iii - Deep Learning In Java - Holczer Balazs/udemy
Artificial Intelligence Iv - Reinforcement Learning In Java - Holczer Balazs/udemy
Search Algorithms in Artificial Intelligence with Java - Udemy
IntrodUCtion To Numerical Methods In Java - Udemy/ Holczer Balazs
Intro to ML - Udacity
Machine Learning - Andrew Ng/ Coursera
Introduction
Linear Regression with One Variable
Linear Algebra Review
Linear Regression with Multiple Variables
Octave/Matlab Tutorial
Logistic Regression
Regularization
Neural Networks: Representation
Neural Networks: Learning
Advice for Applying Machine Learning
Machine Learning System Design
Support Vector Machines
Unsupervised Learning
Dimensionality Reduction
Anomaly Detection
Recommender Systems
Large Scale Machine Learning
Application Example: Photo OCR
Foundations Of Machine Learning/ Bloomberg
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1. Black Box Machine Learning
2. Case Study: Churn Prediction
3. IntrodUCtion To Statistical Learning Theory
4. Stochastic Gradient Descent
5. Excess Risk Decomposition
6. L1 And L2 Regularization
7. Lasso, Ridge, And Elastic Net
8. Loss Functions For Regression And Classification
9. Lagrangian Duality And Convex Optimization
10. Support Vector Machines
11. Subgradient Descent
12. Feature Extraction
13. Kernel Methods
14. Performance Evaluation
15. "Citysense": Probabilistic Modeling For Unusual Behavior Detection
16. Maximum Likelihood Estimation
17. Conditional Probability Models
18. Bayesian Methods
19. Bayesian Conditional Probability Models
20. Classification And Regression Trees
21. Basic Statistics And A Bit Of Bootstrap
22. Bagging And Random Forests
23. Gradient Boosting
24. Multiclass And IntrodUCtion To StrUCtured Prediction
25. K-Means Clustering
26. Gaussian Mixture Models
27. EM Algorithm For Latent Variable Models
28. Neural Networks
29. Backpropagation And The ChAIn Rule
30. Next Steps
CPP Conference 2017: Tour Of Deep Learning With C++ - Peter Goldsborough/ Youtube
Machine Learning In Java - Bostjan Kaluza/ Packt
Natural Language Processing With Java - Richard M. Reese/ Packt
Lazy Programmer Inc - Udemy
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The Numpy Stack In Python - Udemy
Linear Regression In Python - Udemy
Logistic Regression In Python - Udemy
Modern Deep Learning In Python - Udemy
Deep Learning In Python (D-1)- Udemy
Practical Deep Learning In Theano And Tensorflow (D-2) - Udemy
Convolutional Neural Networks In Theano And Tensorflow (D-3)- Udemy
Unsupervised Deep Learning In Python (D-4)- Udemy
Recurrent Neural Networks In Python (D-5)- Udemy
Easy Natural Language Processing In Python - Udemy
Advanced Natural Language Processing In Python (D-6)- Udemy
Support Vector Machines in Python - Udemy
Advanced Computer Vision - Udemy
Artificial Intelligence: Reinforcement Learning In Python - Udemy
Advanced AI: Deep Reinforcement Learning In Python - Udemy
Cutting-Edge AI: Deep Reinforcement Learning in Python - Udemy
Deep Learning: Advanced NLP And RNNs - Udemy
Deep Learning: GANs And Variational Auto-encoders - Udemy
Data Science: Supervised Machiene Learnirng In Python - Udemy
Cluster Analysis And Unsupervised Machine Learning In Python - Udemy
Unsupervised Machien Learning: Hidden Markov Models In Python - Udemy
Bayesian Machien Learning In Python: A/b Testing - Udemy
Ensemble Machine Learning In Python: Random Forest And Adaboost - Udemy
Recommender Systems And Deep Learning In Python - Udemy
Machine Learning and AI: Support Vector Machines in Python - Udemy
Numpy - Scipy Lecture Notes
Pandas/ Data Wrangling - Chris Albon Notes
Reinforcement Learning - Udacity
Udacity Nanodegree
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Deep Reinforcement Learning Nanodegree - Udacity
Artificial Intelligence Nanodegree - Udacity
Machine Learning Nanodegree - Udacity
Predictive Analytics for Business - Udacity
UC Berkley/ Stanford Courses
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Machine Learning (CS229) - Andrew Ng, Ron Dror/ Stanford University
Reinforcement Learning - David Silver, UCL/Youtube
Deep Reinforcement Learning - Sergey Levine/UC Berkeley
Designing, Visualizing And Understanding Deep Learning Network - University Of California, Berkeley
Unsupervised Feature Learning And Deep Learning - Stanford University
Intro To NLP - Dan Jurafsky And Christopher Manning/ Stanford University
Deep Learning For Natural Language Processing - Stanford University
Recommender Systems Specialization - University of Minnesota
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Introduction to Recommender Systems: Non-Personalized and Content-Based
Nearest Neighbor Collaborative Filtering
Recommender Systems: Evaluation and Metrics
Matrix Factorization and Advanced Techniques
Recommender Systems Capstone
Deep Learning Specialization - Andrew Ng/ Coursera
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Neural Networks And Deep Learning - Coursera
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization
And Optimization - Coursera
Structuring Machine Learning Projects - Coursera
Convolutional Neural Networks - Coursera
Sequence Models - Coursera
Weka Framework
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Data Mining With Weka - Future Learn/ Prof Ian H. Witten
Advanced Data Mining With Weka - Future Learn/ Prof Ian H. Witten
More Data Mining With Weka - Future Learn/ Prof Ian H. Witten
Machile Learning Crush Curse - Google
Probabilistic Graphical Models Specialization
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Probabilistic Graphical Models I: Representation - Stanford University
Probabilistic Graphical Models Ii: Inference - Stanford University
Probabilistic Graphical Models Iii: Learning - Stanford University
figure: Zoo of NN
MATHMATICS
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Bloomberg Mathematics Recommendation
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Carlos Fernandez-granda's Mathematics Lecture Notes
Mathematics For Machine Learning - Garrett Thomas/ UC Berkeley
Linear Algebra Review And Reference - Zico Kolter
Review Of Probability Theory - Arian Maleki, Tom Do/ Stanford University
Mathematics Of Machine Learning - MIT
Stanford Mathematics Recommendation
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Linear Algebra Review and Reference - Zico Kolter
Probability Theory Review for Machine Learning - Samuel Ieong
Convex Optimization Overview I - Zico Kolter
Convex Optimization Overview II - Chuong B. Do
Hidden Markov Models Fundamentals - Daniel Ramage
Gaussian processes - Chuong B. Do
Matrix Calculus - ExplAIned.AI
Numerical Linear Algebra (LA) - Princeton University
Mathematics For RL - Lpalmieri.com
Intro To Probability And Statistics - MIT
Linear Algebra - MIT
Single Variable Calculus - MIT
Multi-variable Calculus - MIT
Differential Equations - MIT
Mathematics Of Machine Learning - MIT
Statistical Learning Theory - MIT
Statistical Learning Theory - UC Berkley
Math Background For ML Series - Geoff Gordon/ Carnegie Mellon University, CMU Youtube
ML Mathematics Videos Videos (Visualization Purpsoe) - 3-Blue-1-Brown/ Youtube Videos
Coding The Matrix: Linear Algebra Through Computer Science Application - Brown University/ Philip Klein
Seeing Theory - Brown University [https://seeing-theory.brown.edu/]
Topics In Mathematics With Applications In Finance - MIT
Analytics Of Finance I & II - MIT
Investment - MIT
FINANCE
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Machine Learning For Trading - Udacity
Computational Investing - Coursera
Classification-Based Machine Learning for Finance - Udemy
Stock Technical Analysis with Python - Udemy
Business Statistics with Python - Udemy
Stock Technical Analysis with Excel - Udemy
Stock Fundamental Analysis with Excel - Udemy
Forecasting Models with Excel - Udemy
Quantitative Finance & Algorithmic Trading I - Holczer Balazs/ Udemy
Quantitative Finance & Algorithmic Trading Ii - Holczer Balazs/ Udemy
Pricing Options With Mathematical Models - Caltech/ Edx
ML/ Reinforcement Learning In Finance Specialization - NYU/ Coursera
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Guided Tour Of Machine Learning In Finance - NYU/ Coursera
Fundamentals Of Machine Learning In Finance - NYU/ Coursera
Reinforcement Learning In Finance - NYU/ Coursera
Overview Of Advanced Methods Of Reinforcement Learning In Finance - NYU/ Coursera
Artificial Intelligence For Trading Nanodegree - Udacity
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Term I: Quantitative Trading
P-I: Term I: Quantitative Trading
P-II: Basic Quantitative Trading
P-III: Advanced Quantitative Trading
P-IV: Stocks, Indices, and ETFs
P-V: Multi-factor models
Term II: AI Algorithms in Trading
P-I: Sentiment Analysis with Natural Language Processing
P-II: Advanced Natural Language Processing with Deep Learning
P-III: Simulating Trades with Historical Data
P-IV: Combining Multiple Signals
Quantopian - Education For The Quantitative Finance
BOOKS
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Deep Learning - Ian Goodfellow, Yoshua Bengio and Aaron Courville
Foundations of Data Science - Avrim Blum, John Hopcroft, Ravi Kannan
Bayesian Methods For Hackers: Probabilistic Programming - Cameron Davidson-pilon
Advances in Financial Machine Learning - Marcos Lopez de Prado
An Introduction to Quantitative Finance - Stephen Blyth
Dynamic Hedging: Managing Vanilla and Exotic Options - Nassim Taleb
The Physics of Wall Street: A Brief History of Predicting the Unpredictable - James Owen Weatherall
Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets - Nassim Taleb
PAPERS
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Papers with code - <https://paperswithcode.com/>