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Introduction to ML

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Introduction to Machine Learning

Lets Get Rolling - Student Pre-Read

Before this lesson , we recommend you go through

Learning Objectives

After this lesson, you'll be able to

  • Understand broad categories of Machine Learning Algorithms
  • Understand the Machine Learning Workflow
  • Work with Data

Agenda

  • Merits of 3 Pass System

  • What is Machine Learning?

  • History of Machine Learning

  • Types of Machine Learning

  • The Machine Learning Workflow

  • Scientific Evolution 01

  • Management Evolution 02

    • Henry Ford - Personal Charisma - Key Lieutenants
    • Toyota - Process - 6 Sigma
    • Michael Porter - 5 Forces - Consulting Practices
    • Evolution of Decision Support Systems - Investment Banking
    • Thomas Davenport - Analytics 3.0
    • Tactical Support - Mass Personalization

  • Algorithmic Evolution

  • Overview of key Algorithms

    Deep Learning & Neural Nets

    • 1.1 No. of Layers
    • 1.2 No. of Nodes
    • 1.3 Weights
    • 1.4 Activation Function
    • 1.5 Learning Rate - Algorithms to vary Learning Rates
    • 1.6 Other Hyper Parameters

    Ordinary Least Square

    • 2.1 Regularization
    • 2.2 Shrinkage
    • 2.3 Kernels

    Decision Tree - Entropy based + Marketing Example

    • 3.1 Tree Depth
    • 3.2 Prune
    • 3.3 Stopping Criteria

    Maximum Likelihood - Video1 + Video2 + Video3

    • 4.1 This is a Base Algorithm

    PCA

    • 5.1 No. of Components
    • 5.2 Learning Rate
    • 5.3 Random Seed

    Stochastic Gradient Descent

    • 6.1 Learning rate
    • 6.2 Type of Loss Function
    • 6.3 Penalty

    Clustering - Centroid Based

    • 7.1 No. of clusters
    • 7.2 Algorithm -
    • 7.3 N Jobs - Random Seed

    Ensembles

    • 8.1 Everything in Oridinary Least Square/ Decision Tree and Stochastic Gradient Descent more

  • Thinking about Data

  • Where does Data Come from?

  • How to Access Data?

    • Data from the Web 1 (Web Scraping)
    • Data from the Web 2 (APIs)

Slides

@gslides

Instructors code alongs

Assignments

Complete this setup before attempting the assignments

& many more inside commit.live.

Resources & Post Reads

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Introduction to ML

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