Data Science & Machine Learning Developer

Data Science & Machine Learning Developer - TCS Certification Program

This program provides a comprehensive set of knowledge and skills in data science, machine learning, and deep learning. You learn many tools including Python, Scikit-Learn, Keras and Tensorflow.

Buy $1,250.00

Course curriculum

1. About this course: Data Science & Machine Learning - Developer Certification
2. Kick-off Webinar (February 22, 2019) - Recording
3. Key pointers for success in this course
4. Curriculum Description
5. Instructor Bio: Mark Kerzner
6. Instructor Bio: Tim Fox
7. Instructor Bio: Abishek Ramasubramanian
8. Some added benefits
1. Announcement 001 - Instructions for storing labs, etc.
2. Announcement 002 - Slack Channel Notification
3. Announcement 003 - Statistics Module Forthcoming
4. Announcement 004 - Quizzes >> KEY METRIC FOR SUCCESS IN THIS TRAINING PROGRAM
5. Announcement 005 - Lab Due Dates >> ANOTHER KEY METRIC FOR SUCCESS IN THIS PROGRAM
6. Announcement 006 - Access to Machine Learning course content extended to Dec 31, 2019
7. Announcement 007 and 008 - Statistics and Mathematics Modules
8. ANNOUNCEMENT 009 - Pushing Labs to gitlab.com for review and grading
9. ANNOUNCEMENT 010 - Updated schedule to make live program overlap work hours 100%
10. ANNOUNCEMENT 011 - UPON COMPLETION >> Digital Ninja Honors Certification and Digital Ninja Learner Certification
1. Course Discussion on Slack
1. RECORDING: Office Hours - Week 1, Session 1
2. RECORDING: Live Instruction - Week 1, Session 1
3. RECORDING: Office Hours - Week 1, Session 2
4. RECORDING: Live Instruction - Week 1, Session 2
5. RECORDING: Office Hours - Week 2, Session 1
6. RECORDING: Live Instruction - Week 2, Session 1
7. RECORDING: Office Hours - Week 2, Session 2
8. RECORDING: Office Hours - Week 3, Session 1
9. RECORDING: Live Instruction - Week 3, Session 1
10. RECORDING: Office Hours - Week 3, Session 2
11. RECORDING: Live Instruction - Week 3, Session 2
12. RECORDING: Live Instruction - Week 4, Session 1
13. RECORDING: Office Hours - Week 4, Session 1
14. RECORDING: Office Hours - Week 1, Session 1
15. RECORDING: Live Instruction - Week 5, Session 1
16. RECORDING: Office Hours - Week 5, Session 2
17. RECORDING: Live Instruction - Week 5, Session 2
18. RECORDING: Office Hours - Week 6, Session 1
19. RECORDING: Live Instruction - Week 6, Session 1
20. RECORDING: Office Hours - Week 6, Session 2
21. RECORDING: Live Instruction - Week 6, Session 2
22. RECORDING: Final Challenge Review
1. Labs - Overview
2. Due Dates: Labs
3. INSTRUCTIONS: Virtual labs (For Colaboratory)
4. INSTRUCTIONS: Lab Uploads to TCS servers
5. INSTRUCTIONS: Pushing/grading labs on gitlabs.com [Name your folders Week1, Week2..., and the specific file the name of the lab]
6. Week 1: Labs
7. Week 2: Labs
8. Week 3: Labs
9. Week 4: Labs
10. Week 5: Labs
11. Week 6: Labs
1. Quiz Overview
2. Week 1 Quiz - Due by Saturday, March 9, 2019 at 12 midnight EST
3. Week 2 Quiz - Due by Wednesday, March 13, 2019 at 12 midnight EST
4. Week 3 Quiz - Due by Wednesday, March 20, 2019 at 12 midnight EST
5. Week 4 Quiz - Due by Wednesday, March 27, 2019 at 12 midnight EDT
6. Week 5 Quiz - Due by Wednesday, April 3, 2019 at 12 midnight EDT
7. Week 6 Quiz - Due by Saturday, April 6, 2019 at 12 midnight EDT
1. Statistics and Mathematics for Machine Learning
2. RECORDING: Statistics Module
3. Statistics Module Slides - pdf
1. Week 1 - Focus and Objectives
2. READING: Intro to Machine Learning for Managers (Read Pages 1-12)
3. READING: TCS - Machine First Approach (Read all)
4. READING: TCS Global Trend Study - AI Overview (Skim all)
5. READING: Jeff Dean Rice Talk - State of Artificial Intelligence (Read entire document) (Dated but useful)
6. SLIDES - Week 1 Slides in pdf format (ALL SLIDES FOR WEEK ARE HERE)
7. Lesson 1: Introduction to Machine Learning
8. Lesson 1: Lab 1
9. Lesson 2-1: Pandas
10. Lesson 2-1: Exploring Pandas
11. Lesson 2-1: Lab-2a
12. Lesson 2-2: Lab-2b
13. Lesson 2-2: Lab 2c
14. Lesson 2-3: Visualization
15. Lesson 2-4: Visualization-Stats
16. Lesson 2-4: Lab-2d
17. Lesson 3-1: Sklearn
18. Lesson 3-2: Lab-3b
19. Lesson 3-2: Linear Regression
20. Lesson 3-3: Multivariate Linear Regression
21. Lesson 3-4: Logistic Regression (updated audio)
1. Week 2 - Focus and Objectives
2. READING: ISLR (Read Chapter 8 - Trees)
3. READING: ISLR (Read Chapter 9 - Support Vector Machine)
4. READING: ISLR (Read Chapter 10 - Unsupervised)
5. SLIDES - Week 2 Slides in pdf format (ALL SLIDES FOR WEEK ARE HERE)
6. Lesson 1a: Classification (Support Vector Machines)
7. Lesson 1b: Classification (Naive Bayes)
8. Lesson 2-1: Lab1a and 1b
9. Lesson 2a: Decision Trees
10. Lesson 2b: Random Forests
11. Lesson 2-1: Lab-2a and 2b
12. Lesson 2-1: Lab-2c
13. Lesson 3a: Clustering
14. Lesson 3b: Principal Component Analysis
15. Lesson 2-1: Lab-3a and 3b
16. Lesson 3-1: Lab-3c (Principal Component Analysis)
1. Week 3 - Focus and Objectives
2. READING: Introduction to Deep Learning
3. READING: Introduction to Linear Algebra
4. READING: Introduction to Statistics
5. SLIDES - Week 3 Slides in pdf format (ALL SLIDES FOR WEEK ARE HERE)
6. Lesson 1a: Deep Learning - Intro
7. Lesson 1a: Lab 1a - Tensorflow Playground
8. Lesson 1b: TensorFlow - Intro
9. Lesson 1b: Lab 1b - Tensorflow Sessions
10. Lesson 1c: TensorFlow- Low Level API
11. Lesson 2a: TensorFlow - Linear Models
12. Lesson 2a: Lab 2a and 2b
13. Lesson 2b: TensorFlow - High-Level API
14. Lesson 2b: Lab 2c and 2d
15. Lesson 3a: Lab 3a
16. Lesson 3a: Lab 3b and 3c
17. Lesson 3b: Lab 3d and 3e
1. Week 4 - Focus and Objectives
2. READING: Place of Convolutional Neural Networks (CNN) and Deep Learning
3. READING: Parameter Sharing and CNN
4. READING: Understanding CNN
5. READING: A Brief History of CNNs in Image Segmentation
6. READING: CNN Architectures
7. Lesson 1 - Convolutional Neural Networks
8. Lesson 2 - Convolutional Neural Networks, Extended
9. Lesson 3 - TensorBoard: Visualizing Learning
10. SLIDES - Week 4 Slides in pdf format (ALL SLIDES FOR WEEK ARE HERE)
1. Week 5 - Focus and Objectives
2. READING: An Introduction to Recurrent Neural Networks
3. READING: Sequence Modeling: Recurrent and Recursive Nets
4. READING: Convolutional Neural Networks for Text
5. SLIDES - Week 5 Slides in pdf format (ALL SLIDES FOR WEEK ARE HERE)
6. Lesson 1: Transfer Learning
7. Lesson 2: Recurrent Neural Networks
8. Lesson 3: Long Short-Term Memory (LSTM)
1. Week 6 - Focus and Objectives
2. READING: The State of Machine Learning Adoption in the Enterprise
3. READING: AI Transformation Playbook
4. READING: Machine Learning Yearning - Andrew Ng (Read all)
5. READING: Efﬁcient Estimation o fWord Representations in Vector Space
6. READING: Distributed Representations of Sentences and Documents
7. READING: Linguistic Regularities in Continuous Space Word Representations
8. READING: Distribited Repesentation of Words and Phrases
9. READING: Text Understanding from Scratch
10. Machine Learning: At a Glance
11. SLIDES - Week 6 Slides in pdf format (ALL SLIDES FOR WEEK ARE HERE)
12. Lesson 1: Scaling Machine Learning - Distributed TensorFlow
13. Lesson 2: Feature Engineering
14. Lesson 3: Pipeline Examples
1. Final Coding Challenge: Overview
2. Final Coding Challenge: Instructions
3. Final Coding Challenge: Review and Discussion
4. Tools for the Final Coding Challenge
1. Final Exam: Overview and Instructions
2. Final Exam: Launch here
3. Final Exam has been RECONFIGURED! (with same length and same # questions)
1. Congrats! Here's what's next...
2. Yippee! You're an alumnus!
3. Alumni Slack Channel

About this course

$1,250.00
158 lessons
12.5 hours of video content

Data Science & Machine Learning Developer - TCS Certification Program

Course curriculum

Welcome!

Program Announcements and Notifications

Discussion Channel

Recordings (Live Instruction + Office Hours)

Labs

Quizzes

Week 0 - Stats

Week 1

Week 2

Week 3

Week 4

Week 5

Week 6

Final Coding Challenge

Final Examination

Next steps

About this course