Course curriculum

  • 1
    Introduction to Course
  • 2
    Fundamentals of Machine Learning
  • 3
    Data Preprocessing
  • 4
    Supervised Learning Algorithm
  • 5
    Regression
  • 6
    Decision Tree
  • 7
    Random Forest Regression
  • 8
    Classification
  • 9
    Clustering
  • 10
    Time Series Modeling
  • 11
    Reinforcement Learning
  • 12
    Deep Learning And Artificial Neural Network
  • 13
    Dimensionality Reduction
  • 14
    Communication and Perceiving
  • 15
    Bonus #Project 1
  • 16
    Bonus #Project 2
  • 17
    Study Material
  • Introduction
  • What is Machine Learning? Types Of Machine Learning Setting up the Development Environment
  • Importing Libraries Importing Dataset Taking care of Missing Data Encoding Data: Categorical Data Splitting the dataset into the Training set and Test set Feature Scaling
  • Supervised Learning
  • Simple Linear Regression Polynomial Regression
  • Implementation of Decision Tree
  • Implementation of Random Forest Regression
  • Implementation of Logistic Regression Implementation of Naive Bayes Implementation of Support Vector Machine (SVM)
  • Implementation of k-means clustering Implementation of Hierarchical Clustering Implementation of Apriori
  • Implementation of Time Series Modeling
  • Implementation of Upper Confidence Bound (UCB) Implementation of Thompson Sampling
  • Implementatiom of Artificial Neural Networks (ANN) Implementatiom of Convolutional Neural Network (CNN) Implementation of XGBoost
  • Implementation Principal Component Analysis Implementation Linear Discriminant Analysis Implementation Kernel PCA
  • Implementation of Natural Language Processing in Python
  • Hands-on
  • Hands-on
  • Source Code