Course curriculum

  • 1

    Welcome!

  • 2

    Slack Channel and More

    • Slack Channel (All announcements, Q&A with instructors, etc.)
    • If (and when) you need help...
  • 3

    Module 0: Statistics and Mathematics for Data Science

    • Statistics Module Slides - presentation
  • 4

    Module 1: Introduction to Machine Learning

    • Focus and Objectives
    • READING: Intro to Machine Learning for Managers (Read Pages 1-12)
    • READING: Jeff Dean Rice Talk - State of Artificial Intelligence (Read entire document) (Dated but useful)
    • Module 1 - SLIDES - Part 1
    • Module 1 - SLIDES - Part 2
    • Module 1 - SLIDES - Part 3
    • Module 1 - SLIDES - Part 4
    • Module 1 - SLIDES - Part 5
    • Module 1 - SLIDES - Part 6
    • Module 1 - SLIDES - Part 7
    • Lesson 1: Introduction to Machine Learning
    • Lesson 1: Lab 1
    • Lesson 2-1: Lab-2a
    • Lesson 2-1: Pandas
    • Lesson 2-1: Exploring Pandas
    • Lesson 2-2: Lab-2b
    • Lesson 2-2: Lab 2c
    • Lesson 2-3: Visualization
    • Lesson 2-4: Lab-2d
    • Lesson 2-4: Visualization-Stats
    • Lesson 2-4: Lab 3a
    • Lesson 3-1: Sklearn
    • Lesson 3-2: Lab-3b
    • Lesson 3-2: Linear Regression
    • Lesson 3-3: Multivariate Linear Regression
    • Lesson 3-4: Logistic Regression (updated audio)
  • 5

    Module 2:Exploring and Using Data Sets

    • Module 2 - Focus and Objectives
    • READING: ISLR (Read Chapter 8 - Trees)
    • READING: ISLR (Read Chapter 9 - Support Vector Machine)
    • Lesson 1a: Classification (Support Vector Machines)
    • Lesson 1b: Classification (Naive Bayes)
    • Lesson 2-1: Lab1a and 1b
  • 6

    Module 3: Review of Machine Learning Algorithms

    • Focus and Objectives
    • READING: ISLR (Read Chapter 8 - Trees)
    • READING: ISLR (Read Chapter 9 - Support Vector Machine)
    • READING: ISLR (Read Chapter 10 - Unsupervised)
    • SLIDES - In pdf format (ALL SLIDES AVAILABLE HERE)
    • Module 3 - SLIDES - Part 1
    • Module 3 - SLIDES - Part 2
    • Module 3 - SLIDES - Part 3
    • Module 3 - SLIDES - Part 4
    • Module 3 - SLIDES - Part 5
    • Module 3 - SLIDES - Part 6
    • Lesson 1a: Classification (Support Vector Machines)
    • Lesson 1b: Classification (Naive Bayes)
    • Lesson 2-1: Lab1a and 1b
    • Lesson 2a: Decision Trees
    • Lesson 2b: Random Forests
    • Lesson 2-1: Lab-2a and 2b
    • Lesson 2-1: Lab-2c
    • Lesson 3a: Clustering
    • Lesson 3b: Principal Component Analysis
    • Lesson 2-1: Lab-3a and 3b
    • Lesson 3-1: Lab-3c (Principal Component Analysis)
  • 7

    Module 4: Machine Learning with Scikit

    • Focus and Objectives
    • READING: Introduction to Deep Learning
    • READING: Introduction to Linear Algebra
    • READING: Introduction to Statistics
    • SLIDES - In pdf format (ALL SLIDES AVAILABLE HERE)
    • Module 4 - SLIDES - Part 1
    • Module 4 - SLIDES - Part 2
    • Module 4 - SLIDES - Part 3
    • Module 4 - SLIDES - Part 4
    • Module 4 - SLIDES - Part 5
    • Module 4 - SLIDES - Part 6
    • Module 4 - SLIDES - Part 7
    • Module 4 - SLIDES - Part 8
    • Module 4 - SLIDES - Part 9
    • Module 4 - SLIDES - Part 10
    • Lesson 1a: Deep Learning - Intro
    • Lesson 1a: Lab 1a - Tensorflow Playground
    • Lesson 1b: TensorFlow - Intro
    • Lesson 1b: Lab 1b - Tensorflow Sessions
    • Lesson 1c: TensorFlow- Low Level API
    • Lesson 2a: TensorFlow - Linear Models
    • Lesson 2a: Lab 2a and 2b
    • Lesson 2b: TensorFlow - High-Level API
    • Lesson 2b: Lab 2c and 2d
    • Lesson 3a: Lab 3a
    • Lesson 3a: Lab 3b and 3c
    • Lesson 3b: Lab 3d and 3e
    • Lesson 4: Multilayer Perceptron (MLP)
  • 8

    Module 5: Deep Learning with Keras and TensorFlow

    • Focus and Objectives
    • READING: Place of Convolutional Neural Networks (CNN) and Deep Learning
    • READING: Parameter Sharing and CNN
    • READING: Understanding CNN
    • READING: A Brief History of CNNs in Image Segmentation
    • READING: CNN Architectures
    • SLIDES - In pdf format (ALL SLIDES AVAILABLE HERE)
    • Module 5 - SLIDES - Part 1
    • Lesson 1 - Convolutional Neural Networks
    • Lesson 2 - Convolutional Neural Networks, Extended
    • Lesson 3 - TensorBoard: Visualizing Learning
  • 9

    Module 6: Deeper Understanding of Tensorflow

    • Focus and Objectives
    • READING: An Introduction to Recurrent Neural Networks
    • READING: Sequence Modeling: Recurrent and Recursive Nets
    • READING: Convolutional Neural Networks for Text
    • SLIDES - In pdf format (ALL SLIDES AVAILABLE HERE)
    • Module 5 - SLIDES - Part 1
    • Module 5 - SLIDES - Part 2
    • Module 5 - SLIDES - Part 3
    • Lesson 1: Transfer Learning
    • Lesson 2: Recurrent Neural Networks
    • Lesson 3: Long Short-Term Memory (LSTM)
  • 10

    Module 7: Building a Machine Learning Pipeline

    • Focus and Objectives
    • READING: The State of Machine Learning Adoption in the Enterprise
    • READING: AI Transformation Playbook
    • READING: Machine Learning Yearning - Andrew Ng (Read all)
    • READING: Efficient Estimation o fWord Representations in Vector Space
    • READING: Distributed Representations of Sentences and Documents
    • READING: Linguistic Regularities in Continuous Space Word Representations
    • READING: Distribited Repesentation of Words and Phrases
    • READING: Text Understanding from Scratch
    • READING: Machine Learning: At a Glance
    • SLIDES - In pdf format (ALL SLIDES AVAILABLE HERE)
    • Module 6 - SLIDES - Part 1
    • Module 6 - SLIDES - Part 2
    • Module 6 - SLIDES - Part 3
    • Module 6 - SLIDES - Part 4
    • Lesson 1: Scaling Machine Learning - Distributed TensorFlow
    • Lesson 2: Feature Engineering
    • Lesson 3: Pipeline Examples
  • 11

    Quizzes

    • Overview
    • Quiz 1
    • Quiz 2
    • Quiz 3
    • Quiz 4
    • Quiz 5
    • Quiz 6
  • 12

    Labs

    • Overview
    • INSTRUCTIONS: Virtual labs (For Colaboratory)
    • INSTRUCTIONS: USING JUPYTER NOTEBOOKS
    • Module 1: Labs
    • Module 2: Labs
    • Module 3: Labs
    • Module 4: Labs
    • Module 5: Labs
    • Module 6: Labs
  • 13

    Final Examination

    • Overview and Instructions
  • 14

    Next steps

    • Congrats! Here's what's next...
    • Yippee! You're an alumnus!
    • Alumni Slack Channel