/Machine-Learning-101

Machine Learning 101

Machine-Learning-101

Machine Learning 101 is a central repository intended to give detailed information about the Machine Learning Algorithms and the Math behind these Algorithms with set of use-cases for each. users can use it widely to up-skill them self in Machine learning. This repository is best suite for those who want to make their hands dirty in Machine learning and its Applications.

Machine Learning 101 - The Complete Reference

  1. Introduction
    1. About the Course
    2. Targeted Audience
    3. What you will Learn
    4. What is Machine Learning
    5. Installing Anaconda and Python in your Machine
    6. How to start with Spyder IDE
  2. Data Pre-Processing
    1. Handling Missing Data
    2. Handling Categorical Data
    3. Splitting Data-Set into Dev/Training/Test set
    4. Feature Scaling
  3. Regression
    1. Simple Linear Regression
    2. Multiple Linear Regression
    3. Polynomial Regression
    4. SVR - Support Vector Regression
    5. Decision Tree Regression
      1. ID3
    6. Random Forest Regression
  4. Classification
    1. Logistic Regression
    2. K-NN
    3. SVM - Support Vector Machine
    4. Naive Bayes
    5. Decision Tree Classification
      1. ID3
      2. C4.5 or J4.8
      3. CART
    6. Random Forest Classification
  5. Clustering
    1. K-Means
    2. Hierarchical Clustering
  6. Association Rule Learning
    1. Apriori Rule
    2. Eclat Rule
  7. Reinforcement Learning
    1. UCB - Upper Confidence Bound
    2. Thompson Sampling
  8. Deep Learning
    1. Artificial Neural Networks
    2. Convolutional Neural Networks
  9. Dimensionality Reduction
    1. PCA
    2. LDA
  10. Model Selection & Boosting
    1. k-fold Cross Validation
    2. Parameter Tuning
    3. Grid Search
    4. XGBoost