Machine Learning

• Introduction to Data Science

  • • What is Data Science?
    • What does Data Science involve?
    • Era of Data Science
    • Business Intelligence vs Data Science
    • Life cycle of Data Science
    • Tools of Data Science
    • Introduction to Python
    • Data Extraction, Wrangling, & Visualization
    • Data Analysis Pipeline
    • What is Data Extraction
    • Types of Data
    • Raw and Processed Data
    • Data Wrangling
    • Exploratory Data Analysis
    • Visualization of Data

• Introduction to Machine Learning with Python

  • • Python Revision (numpy, Pandas, scikit learn, matplotlib)
    • What is Machine Learning?
    • Machine Learning Use-Cases
    • Machine Learning Process Flow
    • Machine Learning Categories
    • Linear regression
    • Gradient descent

• Supervised Learning - I

  • • What is Classification and its use cases?
    • What is Decision Tree?
    • Algorithm for Decision Tree Induction
    • Creating a Perfect Decision Tree
    • Confusion Matrix
    • What is Random Forest?

• Dimensionality Reduction

  • • Introduction to Dimensionality
    • Why Dimensionality Reduction
    • PCA
    • Factor Analysis
    • Scaling dimensional model
    • LDA

• Supervised Learning - II

  • • What is Naïve Bayes?
    • How Naïve Bayes works?
    • Implementing Naïve Bayes Classifier
    • What is Support Vector Machine?
    • Illustrate how Support Vector Machine works?
    • Hyperparameter optimization
    • Grid Search vs Random Search
    • Implementation of Support Vector Machine for Classification

• Unsupervised Learning

  • • What is Clustering & its Use Cases?
    • What is K-means Clustering?
    • How does K-means algorithm works?
    • How to do optimal clustering
    • What is C-means Clustering?
    • What is Hierarchical Clustering?
    • How Hierarchical Clustering works?

• Association Rules Mining and Recommendation Systems

  • • What are Association Rules?
    • Association Rule Parameters
    • Calculating Association Rule Parameters
    • Recommendation Engines
    • How Do Recommendation Engines work?
    • Collaborative Filtering
    • Content Based Filtering

• Reinforcement Learning

  • • What is Reinforcement Learning
    • Why Reinforcement Learning
    • Elements of Reinforcement Learning
    • Exploration vs Exploitation dilemma
    • Epsilon Greedy Algorithm
    • Markov Decision Process (MDP)
    • Q values and V values
    • Q ? Learning
    • ? values

• Time Series Analysis

  • • What is Time Series Analysis?
    • Importance of TSA
    • Components of TSA
    • White Noise
    • AR model
    • MA model
    • ARMA model
    • ARIMA model
    • Stationarity
    • ACF & PACF

• Model Selection and Boosting

  • • What is Model Selection?
    • Need of Model Selection
    • Cross ? Validation
    • What is Boosting?
    • How Boosting Algorithms work?
    • Types of Boosting Algorithms
    • Adaptive Boosting