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This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources. Other awesome lists can be found in this
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If you want to contribute to this list, please read Contributing Guidelines.
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Curated list of R tutorials for Data Science, NLP and Machine Learning). -
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Curated list of Python tutorials for Data Science, NLP and Machine Learning).
- Introduction
- Interview Resources
- Artificial Intelligence
- Genetic Algorithms
- Statistics
- Useful Blogs
- Resources on Quora
- Resources on Kaggle
- Cheat Sheets
- Classification
- Linear Regression
- Logistic Regression
- Model Validation using Resampling
- Deep Learning
- Natural Language Processing
- Computer Vision
- Support Vector Machine
- Reinforcement Learning
- Decision Trees
- Random Forest / Bagging
- Boosting
- Ensembles
- Stacking Models
- VC Dimension
- Bayesian Machine Learning
- Semi Supervised Learning
- Optimizations
- Other Useful Tutorials
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π Machine Learning Course by Andrew Ng (Stanford University)
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AI/ML YouTube Courses) -
In-depth introduction to machine learning in 15 hours of expert videos
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List of Machine Learning University Courses) -
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Machine Learning for Software Engineers) -
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Dive into Machine Learning) -
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A curated list of awesome Machine Learning frameworks, libraries and software) -
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A curated list of awesome data visualization libraries and resources.) -
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An awesome Data Science repository to learn and apply for real world problems) -
π Machine Learning algorithms that you should always have a strong understanding of
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Difference between Linearly Independent, Orthogonal, and Uncorrelated Variables
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TheAnalyticsEdge edX Notes and Codes) -
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Have Fun With Machine Learning) -
Twitter's Most Shared #machineLearning Content From The Past 7 Days
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π 41 Essential Machine Learning Interview Questions (with answers)
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π How can a computer science graduate student prepare himself for data scientist interviews?
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Awesome Artificial Intelligence (GitHub Repo)) -
π Programming Community Curated Resources for learning Artificial Intelligence
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π MIT 6.034 Artificial Intelligence Lecture Videos, π Complete Course
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Simple Implementation of Genetic Algorithms in Python (Part 1), Part 2
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π Genetic Programming
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Genetic Programming in Python (GitHub)) -
π Genetic Alogorithms vs Genetic Programming (Quora), StackOverflow
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Stat Trek Website - A dedicated website to teach yourselves Statistics
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Learn Statistics Using Python) - Learn Statistics using an application-centric programming approach -
π Statistics for Hackers | Slides | @jakevdp - Slides by Jake VanderPlas
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Online Statistics Book - An Interactive Multimedia Course for Studying Statistics
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Tutorials
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π OpenIntro Statistics - Free PDF textbook
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Edwin Chen's Blog - A blog about Math, stats, ML, crowdsourcing, data science
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The Data School Blog - Data science for beginners!
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ML Wave - A blog for Learning Machine Learning
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Andrej Karpathy - A blog about Deep Learning and Data Science in general
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Colah's Blog - Awesome Neural Networks Blog
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Alex Minnaar's Blog - A blog about Machine Learning and Software Engineering
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Statistically Significant - Andrew Landgraf's Data Science Blog
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Simply Statistics - A blog by three biostatistics professors
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π Yanir Seroussi's Blog - A blog about Data Science and beyond
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fastML - Machine learning made easy
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Trevor Stephens Blog - Trevor Stephens Personal Page
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no free hunch | kaggle - The Kaggle Blog about all things Data Science
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A Quantitative Journey | outlace - learning quantitative applications
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r4stats - analyze the world of data science, and to help people learn to use R
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Variance Explained - David Robinson's Blog
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AI Junkie - a blog about Artificial Intellingence
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Deep Learning Blog by Tim Dettmers - Making deep learning accessible
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J Alammar's Blog- Blog posts about Machine Learning and Neural Nets
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π Adam Geitgey - Easiest Introduction to machine learning
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Ethen's Notebook Collection) - Continuously updated machine learning documentations (mainly in Python3). Contents include educational implementation of machine learning algorithms from scratch and open-source library usage
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Machine Learning Cheat Sheet) -
π ML Compiled
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Multicollinearity and VIF
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π Elastic Net
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Difference between logit and probit models, π Logistic Regression Wiki, π Probit Model Wiki
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Pseudo R2 for Logistic Regression, How to calculate, Other Details
- Cross Validation
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Overfitting and Cross Validation
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A curated list of awesome Deep Learning tutorials, projects and communities) -
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Deep Learning Papers Reading Roadmap) -
Interesting Deep Learning and NLP Projects (Stanford), Website
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π Understanding Natural Language with Deep Neural Networks Using Torch
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Recent Reddit AMAs related to Deep Learning, π Another AMA
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Introduction to Deep Learning Using Python (GitHub)), π Good Introduction Slides -
π Video Lectures Oxford 2015, Video Lectures Summer School Montreal
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Neural Machine Translation
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Machine Translation Reading List) -
π Introduction to Neural Machine Translation with GPUs (part 1), π Part 2, π Part 3
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π Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learning
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Deep Learning Frameworks
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All Codes) -
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Deep Learning Implementation Tutorials - Keras and Lasagne)
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Torch ML Tutorial,
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Learning Torch GitHub Repo) -
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Awesome-Torch (Repository on GitHub)) -
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Torch Cheatsheet) -
Understanding Natural Language with Deep Neural Networks Using Torch
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Caffe
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TensorFlow
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TensorFlow Examples for Beginners) -
π Stanford Tensorflow for Deep Learning Research Course
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GitHub Repo)
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Simplified Scikit-learn Style Interface to TensorFlow) -
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Learning TensorFlow GitHub Repo) -
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Benchmark TensorFlow GitHub) -
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Awesome TensorFlow List) -
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TensorFlow Book) -
π Android TensorFlow Machine Learning Example
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GitHub Repo)
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π Creating Custom Model For Android Using TensorFlow
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GitHub Repo)
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Feed Forward Networks
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Implementing a Neural Network from scratch,
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Code) -
Speeding up your Neural Network with Theano and the gpu,
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Code) -
π Basic ANN Theory
- Recurrent and LSTM Networks
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awesome-rnn: list of resources (GitHub Repo)) -
Recurrent Neural Net Tutorial Part 1, Part 2, Part 3,
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Code) -
The Unreasonable effectiveness of RNNs,
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Torch Code), π Python Code -
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Music generation using RNNs (Keras)) -
Long Short Term Memory (LSTM)
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π LSTM explained
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Implementing LSTM from scratch,
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Python/Theano code) -
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Torch Code for character-level language models using LSTM) -
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LSTM for Kaggle EEG Detection competition (Torch Code)) -
Deep Learning for Visual Q&A | LSTM | CNN,
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LSTM dramatically improves Google Voice Search, Another Article
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Torch code for Visual Question Answering using a CNN+LSTM model) -
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LSTM for Human Activity Recognition)
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Gated Recurrent Units (GRU)
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Time series forecasting with Sequence-to-Sequence (seq2seq) rnn models)
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Restricted Boltzmann Machine
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Autoencoders: Unsupervised (applies BackProp after setting target = input)
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Convolutional Neural Networks
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π An Intuitive Explanation of Convolutional Neural Networks
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Awesome Deep Vision: List of Resources (GitHub)) -
Stanford Notes, Codes,
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GitHub)
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Network Representation Learning
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Awesome Graph Embedding) -
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Awesome Network Embedding) -
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Knowledge Representation Learning Papers) -
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Graph Based Deep Learning Literature)
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A curated list of speech and natural language processing resources) -
Understanding Natural Language with Deep Neural Networks Using Torch
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π Bag of Words
- Topic Modeling
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π LDA Wikipedia, π LSA Wikipedia, π Probabilistic LSA Wikipedia
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π What is a good explanation of Latent Dirichlet Allocation (LDA)?
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π Original LDA Paper
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Multilingual Latent Dirichlet Allocation (LDA)). (Β Β Β Β 82β
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Tutorial here)) -
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Deep Belief Nets for Topic Modeling) -
Python
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Series of lecture notes for probabilistic topic models written in ipython notebook)Β Β Β 369β
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Implementation of various topic models in Python)
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word2vec
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π Google word2vec
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π word2vec Tutorial
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π Word Vectors Kaggle Tutorial Python, π Part 2
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π Quora word2vec
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π Other Quora Resources, π 2, π 3
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Text Clustering
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Text Classification
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Named Entity Recognitation
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π Kaggle Tutorial Bag of Words and Word vectors, π Part 2, π Part 3
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Awesome computer vision (github)) -
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Awesome deep vision (github))
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Comparisons
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Software
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π LIBSVM
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Kernels
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Probabilities post SVM
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Awesome Reinforcement Learning (GitHub))
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What is entropy and information gain in the context of building decision trees?
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How do decision tree learning algorithms deal with missing values?
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π Using Surrogates to Improve Datasets with Missing Values
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π Good Article
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Discover structure behind data with decision trees - Grow and plot a decision tree to automatically figure out hidden rules in your data
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Comparison of Different Algorithms
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CART
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CTREE
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CHAID
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MARS
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Probabilistic Decision Trees
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Awesome Random Forest (GitHub)**) -
π Evaluating Random Forests for Survival Analysis Using Prediction Error Curve
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Why doesn't Random Forest handle missing values in predictors?
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Gradient Boosting Machine
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xgboost
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AdaBoost
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π AdaBoost Wiki, π Python Code
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π adaBag R package
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CatBoost
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π Benchmarks
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Tutorial)
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Ensembling models with R, Ensembling Regression Models in R, Intro to Ensembles in R
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π Good Resources | Kaggle Africa Soil Property Prediction
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Bayesian Methods for Hackers (using pyMC)) -
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Kalman & Bayesian Filters in Python)
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π Video Tutorial Weka
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Mean Variance Portfolio Optimization with R and Quadratic Programming
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Hyperopt tutorial for Optimizing Neural Networksβ Hyperparameters
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For a collection of Data Science Tutorials using R, please refer to
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this list). -
For a collection of Data Science Tutorials using Python, please refer to
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this list).
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ujjwalkarn/Machine-Learning-Tutorials)