Course curriculum

  • 1

    Welcome!

    • About this course: Data Science & Machine Learning - Developer Certification
    • Kick-off Webinar (February 22, 2019) - Recording
    • Key pointers for success in this course
    • Curriculum Description
    • Instructor Bio: Mark Kerzner
    • Instructor Bio: Tim Fox
    • Instructor Bio: Abishek Ramasubramanian
    • Some added benefits
  • 2

    Program Announcements and Notifications

    • Announcement 001 - Instructions for storing labs, etc.
    • Announcement 002 - Slack Channel Notification
    • Announcement 003 - Statistics Module Forthcoming
    • Announcement 004 - Quizzes >> KEY METRIC FOR SUCCESS IN THIS TRAINING PROGRAM
    • Announcement 005 - Lab Due Dates >> ANOTHER KEY METRIC FOR SUCCESS IN THIS PROGRAM
    • Announcement 006 - Access to Machine Learning course content extended to Dec 31, 2019
    • Announcement 007 and 008 - Statistics and Mathematics Modules
    • ANNOUNCEMENT 009 - Pushing Labs to gitlab.com for review and grading
    • ANNOUNCEMENT 010 - Updated schedule to make live program overlap work hours 100%
    • ANNOUNCEMENT 011 - UPON COMPLETION >> Digital Ninja Honors Certification and Digital Ninja Learner Certification
  • 3

    Calendar, Slack Channel/Discussion Forum and More

    • Course Calendar
    • Schedule: Live Instruction and Office Hours (Version 7.0 as of March 26, 2019)
    • Slack Channel - Discussion Forum (All announcements, Q&A with instructors, etc.)
    • If (and when) you need help...
  • 4

    Labs

    • Labs - Overview
    • Due Dates: Labs
    • INSTRUCTIONS: Virtual labs (For Colaboratory)
    • INSTRUCTIONS: Lab Uploads to TCS servers
    • INSTRUCTIONS: Pushing/grading labs on gitlabs.com [Name your folders Week1, Week2..., and the specific file the name of the lab]
    • Week 1: Labs
    • Week 2: Labs
    • Week 3: Labs
    • Week 4: Labs
    • Week 5: Labs
    • Week 6: Labs
  • 5

    Recordings (Live Instruction + Office Hours)

    • RECORDING: Office Hours - Week 1, Session 1
    • RECORDING: Live Instruction - Week 1, Session 1
    • RECORDING: Office Hours - Week 1, Session 2
    • RECORDING: Live Instruction - Week 1, Session 2
    • RECORDING: Office Hours - Week 2, Session 1
    • RECORDING: Live Instruction - Week 2, Session 1
    • RECORDING: Office Hours - Week 2, Session 2
    • RECORDING: Office Hours - Week 3, Session 1
    • RECORDING: Live Instruction - Week 3, Session 1
    • RECORDING: Office Hours - Week 3, Session 2
    • RECORDING: Live Instruction - Week 3, Session 2
    • RECORDING: Live Instruction - Week 4, Session 1
    • RECORDING: Office Hours - Week 4, Session 1
    • RECORDING: Office Hours - Week 1, Session 1
    • RECORDING: Live Instruction - Week 5, Session 1
    • RECORDING: Office Hours - Week 5, Session 2
    • RECORDING: Live Instruction - Week 5, Session 2
    • RECORDING: Office Hours - Week 6, Session 1
    • RECORDING: Live Instruction - Week 6, Session 1
    • RECORDING: Office Hours - Week 6, Session 2
    • RECORDING: Live Instruction - Week 6, Session 2
    • RECORDING: Final Challenge Review
  • 6

    Quizzes

    • Quiz Overview
    • Week 1 Quiz - Due by Saturday, March 9, 2019 at 12 midnight EST
    • Week 2 Quiz - Due by Wednesday, March 13, 2019 at 12 midnight EST
    • Week 3 Quiz - Due by Wednesday, March 20, 2019 at 12 midnight EST
    • Week 4 Quiz - Due by Wednesday, March 27, 2019 at 12 midnight EDT
    • Week 5 Quiz - Due by Wednesday, April 3, 2019 at 12 midnight EDT
    • Week 6 Quiz - Due by Saturday, April 6, 2019 at 12 midnight EDT
  • 7

    Week 0 - Stats

    • Statistics and Mathematics for Machine Learning
    • RECORDING: Statistics Module
    • Statistics Module Slides - pdf
  • 8

    Week 1

    • Week 1 - Focus and Objectives
    • READING: Intro to Machine Learning for Managers (Read Pages 1-12)
    • READING: TCS - Machine First Approach (Read all)
    • READING: TCS Global Trend Study - AI Overview (Skim all)
    • READING: Jeff Dean Rice Talk - State of Artificial Intelligence (Read entire document) (Dated but useful)
    • SLIDES - Week 1 Slides in pdf format (ALL SLIDES FOR WEEK ARE HERE)
    • Lesson 1: Introduction to Machine Learning
    • Lesson 1: Lab 1
    • Lesson 2-1: Pandas
    • Lesson 2-1: Exploring Pandas
    • Lesson 2-1: Lab-2a
    • Lesson 2-2: Lab-2b
    • Lesson 2-2: Lab 2c
    • Lesson 2-3: Visualization
    • Lesson 2-4: Visualization-Stats
    • Lesson 2-4: Lab-2d
    • 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)
  • 9

    Week 2

    • Week 2 - Focus and Objectives
    • READING: ISLR (Read Chapter 8 - Trees)
    • READING: ISLR (Read Chapter 9 - Support Vector Machine)
    • READING: ISLR (Read Chapter 10 - Unsupervised)
    • SLIDES - Week 2 Slides in pdf format (ALL SLIDES FOR WEEK ARE HERE)
    • 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)
  • 10

    Week 3

    • Week 3 - Focus and Objectives
    • READING: Introduction to Deep Learning
    • READING: Introduction to Linear Algebra
    • READING: Introduction to Statistics
    • SLIDES - Week 3 Slides in pdf format (ALL SLIDES FOR WEEK ARE HERE)
    • 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
  • 11

    Week 4

    • Week 4 - 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
    • Lesson 1 - Convolutional Neural Networks
    • Lesson 2 - Convolutional Neural Networks, Extended
    • Lesson 3 - TensorBoard: Visualizing Learning
    • SLIDES - Week 4 Slides in pdf format (ALL SLIDES FOR WEEK ARE HERE)
  • 12

    Week 5

    • Week 5 - Focus and Objectives
    • READING: An Introduction to Recurrent Neural Networks
    • READING: Sequence Modeling: Recurrent and Recursive Nets
    • READING: Convolutional Neural Networks for Text
    • SLIDES - Week 5 Slides in pdf format (ALL SLIDES FOR WEEK ARE HERE)
    • Lesson 1: Transfer Learning
    • Lesson 2: Recurrent Neural Networks
    • Lesson 3: Long Short-Term Memory (LSTM)
  • 13

    Week 6

    • Week 6 - 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
    • Machine Learning: At a Glance
    • SLIDES - Week 6 Slides in pdf format (ALL SLIDES FOR WEEK ARE HERE)
    • Lesson 1: Scaling Machine Learning - Distributed TensorFlow
    • Lesson 2: Feature Engineering
    • Lesson 3: Pipeline Examples
  • 14

    Final Coding Challenge

    • Final Coding Challenge: Overview
    • Final Coding Challenge: Instructions
    • Final Coding Challenge: Review and Discussion
    • Tools for the Final Coding Challenge
  • 15

    Final Examination

    • Final Exam: Overview and Instructions
    • Final Exam: Launch here
    • Final Exam has been RECONFIGURED! (with same length and same # questions)
  • 16

    Next steps

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