Machine Learning

Starter guide for machine learning by Mozneurons

Content Covered

• Libraries used: Numpy, Matplotlib, Pandas, Scikit-learn,TensorFlow
• Machine Learning Algorithms to be covered:
  • Classification :KNN classification and Naive-Bayes Classifier
  • Regression: Linear regression and Logistic regression
  • Clustering: K-means clustering
  • Deep Learning: Simple Neural-Network,CNN,RNN

Day-wise content covered

• Module 1 - Overview With Cheatsheets :

  • Python Overview
  • Library Basics:
    • Numpy
    • Pandas
    • Matplotlib
    • TensorFlow

• Module 2 - Data Preprocessing :

  • Data Preprocessing with:
    • Scikit-learn
    • Pandas
  • Supervised Vs Unsupervised

• Module 3/4 - Supervised Learning Methods :

  • Supervised method
    1. Classification method
       • Logistic Regression
       • K-Nearest Neighbours(K-NN)
       • Support Vector Machine(SVM)
       • Kernel SVM
       • Naive Bayes
       • Decision Tree Classification
       • Random Forest Classification
    2. Regression method
       • Simple Linear Regression
       • Multiple Linear Regression
       • Polynomial Regression
       • Support Vector for Regression
       • Decision Tree Regression
       • Random Forest Regression

• Module 5 - Unsupervised Learning Methods :

  • Clustering Basics
  • Clustering Types
  • Kmeans Clustering with python code
  • A brush-up of different types of clustering

• Module 6 - Neural Network/Deep Learning :

  • Perceptron
  • Activation functions
  • Simple ANN using Keras
  • CNN
  • RNN

Hey There! This is just the start. Stay tuned! :)