Course curriculum

  • 1

    Module 01: Course Introduction

    • Segment - 01 - What is machine Learning?
    • Segment - 02 - For Mac Users
    • Segment - 03 - Introduction to Python
    • Segment - 04 - IPython in Browser
    • Segment - 05 - Python Data science to be used
  • 2

    Module 02: Read in Data from different courses with Pandas

    • Segment - 06 - What are Pandas?
    • Segment - 07 - Read in Data from CSV
    • Segment - 08 - Read in Online CSV
    • Segment - 09 - Read in Excel Data
    • Segment - 10 - Read in HTML Data
    • Segment - 11 - Read in Data from Databases
  • 3

    Module 03: Data Cleaning and Munging

    • Segment - 12 - Remove Missing Values
    • Segment - 13 - Conditional Data Selection
    • Segment - 14 - Data Grouping
    • Segment - 15 - Data Subsetting
    • Segment - 16 - Ranking & Sorting
    • Segment - 17 - Concatenate
    • Segment - 18 - Merging & Joining Data Frames
  • 4

    Module 04: Unsupervised Learning in Python

    • Segment - 19 - Unsupervised Classification- Some Basic Concepts
    • Segment - 20 - K-Means Clustering: Theory
    • Segment - 21 - Implement K-Means on the Iris Data
    • Segment - 22 - Quantifying K-Means Clustering Performance
    • Segment - 23 - How To Select the Optimal Number of Clusters?
    • Segment - 24 - Hierarchical Clustering - Theory
    • Segment - 25 - Hierarchical Clustering-practical
  • 5

    Module 05: Dimension Reduction & Feature Selection for Machine Learning

    • Segment - 26 - Principal Component Analysis (PCA) - Theory
    • Segment - 27 - Principal Component Analysis (PCA)-Case Study 1
    • Segment - 28 - Principal Component Analysis (PCA)-Case Study 2
    • Segment - 29 - Linear Discriminant Analysis(LDA) for Dimension Reduction
    • Segment - 30 - T-SNE Dimension Reduction
    • Segment - 31 - Feature Selection to Select the Most Relevant Predictors
    • Segment - 32 - Recursive Feature Elimination (RFE)
  • 6

    Module 06: Supervised Learning: Classification

    • Segment - 33 - Concepts Behind Supervised Learning
    • Segment - 34 - Data Preparation for Supervised Learning
    • Segment - 35 - Pointers on Evaluating the Accuracy of Classification Modelling
    • Segment - 36 - Using Logistic Regression as a Classification Model
    • Segment - 37 - KNN - Classification
    • Segment - 38 - Naive Bayes Classification
    • Segment - 39 - Linear Discriminant Analysis
    • Segment - 40 - SVM- Linear Classification
    • Segment - 41 - Non-Linear SVM Classification
  • 7

    Module 07: Neural Networks and Deep Learning Based Classification Techniques

    • Segment - 42 - Perceptrons for Binary Classification
    • Segment - 43 - Artificial Neural Networks (ANN) for Binary Classification
    • Segment - 44 - Multi-class Classification With MLP
    • Segment - 45 - Introduction to H20
    • Segment - 46 - Use H20 for Deep Learning Classification
    • Segment - 47 - Specify the Activation Function
    • Segment - 49 - H20 Deep Learning for Classification