INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
  • Welcome to Clustering & Classification with Machine Learning in Python
  • What is Machine Learning?
  • Data and Scripts For the Course
  • Python Data Science Environment
  • For Mac Users
  • Introduction to IPython
  • IPython in Browser
  • Python Data Science Packages To Be Used
Read in Data From Different Sources With Pandas
  • What are Pandas?
  • Read in Data from CSV
  • Read in Online CSV
  • Read in Excel Data
  • Read in HTML Data
  • Read in Data from Databases
Data Cleaning & Munging
  • Remove Missing Values
  • Conditional Data Selection
  • Data Grouping
  • Data Subsetting
  • Ranking & Sorting
  • Concatenate
  • Merging & Joining Data Frames
Unsupervised Learning in Python
  • Unsupervised Classification- Some Basic Concepts
  • K-Means Clustering:Theory
  • Implement K-Means on the Iris Data
  • Quantifying K-Means Clustering Performance
  • K-Means Clustering with Real Data
  • How To Select the Optimal Number of Clusters?
  • Gaussian Mixture Modelling (GMM)
  • Hierarchical Clustering-theory
  • Hierarchical Clustering-practical
Dimension Reduction & Feature Selection for Machine Learning
  • Principal Component Analysis (PCA)-Theory
  • Principal Component Analysis (PCA)-Case Study 1
  • Principal Component Analysis (PCA)-Case Study 2
  • Linear Discriminant Analysis(LDA) for Dimension Reduction
  • t-SNE Dimension Reduction
  • Feature Selection to Select the Most Relevant Predictors
  • Recursive Feature Elimination (RFE)
Supervised Learning: Classification
  • Concepts Behind Supervised Learning
  • Data Preparation for Supervised Learning
  • Pointers on Evaluating the Accuracy of Classification Modelling
  • Using Logistic Regression as a Classification Model
  • kNN- Classification
  • Naive Bayes Classification
  • Linear Discriminant Analysis
  • SVM- Linear Classification
  • Non-Linear SVM Classification
  • RF-Classification
  • Gradient Boosting Machine (GBM)
  • Voting Classifier
Neural Networks and Deep Learning Based Classification Techniques
  • Perceptrons for Binary Classification
  • Artificial Neural Networks (ANN) for Binary Classification
  • Multi-class Classification With MLP
  • Introduction to H20
  • Use H20 for Deep Learning Classification
  • Specify the Activation Function
  • H20 Deep Learning for Classification
Miscellaneous Information
  • Using Colabs for Online Jupyter Notebooks
  • Github