DELETE Machine Learning Certification

In 12 intensive weeks, you will gain the skills necessary to speak the Machine Learning language, build the toolkit you need to enter the Machine Learning market, and take your career to the next level.

Program curriculum

1. Program overview
2. About Zaka
3. Meet your Zaka support team!
4. Download your MLC Boarding Kit
5. Program syllabus
6. Best practices & tips for success
7. Are you ready?
1. Week kick-off
2. MLC Launch Webinar - Recording Available
3. Prepwork - Get Ready!
4. Guidelines to Google Colaboratory's environment
5. Download - Prepwork cheat sheet
6. Download - Capstone Project Requirements
1. Welcome to Week 1!
2. Outline of the week
3. Week Schedule & Important Notes
4. Project Expectations Webinar - Recording available
5. Download - Week 1 Slides
6. Data Science Lifecycle
7. Test your Zaka - DS Lifecycle
8. EDA Overview
9. Basic steps of EDA
10. Test your Zaka - EDA
11. Data Munging & Wrangling
12. Test your Zaka - Data Munging
13. Data Visualization
14. Case Study: Covid-19 outbreak visualization
15. Test your Zaka - Data Visualization
16. Visualizations with Matplotlib
17. Important Reminder!
18. Tip! - Refer to Python's documentation
19. Hands-on: Visualizations with Matplotlib - part 1
20. Hands-on: Visualizations with Matplotlib - part 2
21. Hands-on: Visualizations with Matplotlib - part 3
22. Additional Practice: Visualizations with Seaborn
23. Data Cleaning
24. Data Cleaning - Step 1: Diagnosis
25. Data Cleaning - Step 2: Cleaning
26. Data Cleaning - Step 3: Finding Correlations
27. Test your Zaka - Data cleaning
28. Hands-on: Data Cleaning - Introduction
29. Hands-on: Data Cleaning - part 1
30. Hands-on: Data Cleaning - part 2
31. Hands-on: Data Cleaning - part 3
32. Hands-on: Data Cleaning - part 4
33. Tip! - Begin your weekly challenge
34. Data Manipulation
35. Test your Zaka - Data Manipulation
36. Hands-on: Data Manipulation - Introduction
37. Hands-on: Data Manipulation - part 1
38. Hands-on: Data Manipulation - part 2
39. Hands-on: Data Manipulation - part 3
40. Hands-on: Data Transformation - Introduction
41. Hands-on: Data Transformation
42. Feature Engineering
43. Issues with Features
44. Test your Zaka - Feature Engineering
45. Documentation & Reporting
46. Your Full Data Science Project
47. Hands-on: Full Data Science Project - Introduction
48. Hands-on: Full Data Science Project - EDA - part 1
49. Hands-on: Full Data Science Project - EDA - part 2
50. Hands-on: Full Data Science Project - EDA - part 3
51. Hands-on: Full Data Science Project - EDA - part 4
52. Hands-on: Full Data Science Project - Data Cleaning - part 1
53. Hands-on: Full Data Science Project - Data Cleaning - part 2
54. Hands-on: Full Data Science Project - Data Cleaning - part 3
55. Hands-on: Full Data Science Project - Modeling - part 1
56. Hands-on: Full Data Science Project - Modeling - part 2
57. Hands-on: Full Data Science Project - Modeling - part 3
58. Data Acquisition
59. Data Types
60. Test your Zaka - Data Types
61. Databases
62. Data Warehouses
63. Test your Zaka - Databases & Data Warehouses
64. Challenge of the Week!
65. Download - Challenge Requirements
66. Download - Week's Run-through
67. Download - Week's Deep Dive
1. Welcome to Week 2!
2. Outline of the week
3. Week Schedule & Important Notes
4. Tuesday-July 13-Office Hours - Recording available
5. Download - Week 2 Slides
6. Intro to Machine Learning
7. Classes of Machine Learning
8. Types of ML Algorithms
9. Test your Zaka - Intro to ML
10. Supervised Learning
11. Types of Supervised Learning
12. Supervised Learning Applications
13. Test your Zaka - Supervised Learning
14. k-Nearest Neighbors
15. Test your Zaka - kNN
16. Linear Regression
17. Error Functions & Evaluation Metrics for Regression
18. How to find the best fitting line?
19. Test your Zaka - Linear Regression
20. Gradient Descent
21. Cost Function Derivative
22. Test your Zaka - Gradient Descent
23. Hands-on: Linear Regression from Scratch - Introduction
24. Hands-on: Linear Regression from Scratch - part 1
25. Hands-on: Linear Regression from Scratch - part 2
26. Tip! - Begin your weekly challenge
27. Multi-linear Regression
28. Logistic Regression
29. Error Functions for Classification
30. Test your Zaka - Logistic Regression
31. Evaluation Metrics for Classifiers
32. Test your Zaka - Evaluation Metrics for Classifiers
33. Hands-on: Logistic Regression from Scratch - Introduction
34. Hands-on: Logistic Regression from Scratch - part 1
35. Hands-on: Logistic Regression from Scratch - part 2
36. What do ML models have in common?
37. Challenge of the Week!
38. Download - Challenge Requirements
39. Download - Week's Run-through
40. Download - Week's Deep Dive
1. Welcome to Week 3!
2. Outline of the week
3. Week Schedule & Important Notes
4. Download - Week 3 Slides
5. Naive Bayes
6. Naive Bayes - Example
7. Tip! - Solve Examples by Hand
8. Test Your Zaka: Naive Bayes
9. Decision Trees
10. How to Build Decision Trees? - Intuition
11. Method 1: Information Gain
12. Method 2: Gini Index
13. Decision Forests
14. Test Your Zaka: Decision Trees
15. Hands-on: Naive Bayes & Decision Trees - Introduction
16. Hands-on: Naive Bayes & Decision Trees - Data Loading
17. Hands-on: Naive Bayes Implementation
18. Hands-on: Decision Trees Implementation
19. Support Vector Machines (SVMs)
20. SVM Loss
21. Non-linear SVM & Kernel Transformations
22. Test Your Zaka: SVM
23. Hands-on: SVM - Introduction
24. Hands-on: SVM - Data Loading
25. Hands-on: Linear SVM
26. Hands-on: Non-linear SVM
27. Intro to Unsupervised Learning
28. Clustering Methods
29. K-means Clustering
30. Test Your Zaka: Clustering Methods
31. Association Analysis
32. Association Analysis - Example
33. Test Your Zaka: Association Analysis
34. Hands-on Workshop on Unsupervised Learning - part 1 - Recording available
35. Hands-on Workshop on Unsupervised Learning - part 2 - Recording available
36. Challenge of the Week!
37. Download - Challenge Requirements (Part A)
38. Important Reminder - Solve Part A first!
39. Download - Challenge Requirements (Part B)
40. Download - Week's Run-through
41. Download - Week's Deep Dive
1. Welcome to Week 4!
2. Outline of the week
3. Week Schedule & Important Notes
4. Human-centered Design Thinking Workshop - part 1 - Recording available
5. Human-centered Design Thinking Workshop - part 1 - Slides available
6. Download - Week 4 Slides
7. Evaluating ML Models
8. Holdout Method
9. K-Fold Method
10. Evaluation Metrics - Review
11. Test your Zaka - Evaluating ML Models
12. Hands-on: Evaluating ML Models - introduction
13. Hands-on: Evaluating ML Models
14. Comparing ML Models
15. Z-Score Test
16. McNemar Test
17. Test your Zaka - Comparing ML Models
18. Hands-on: Comparing ML Models - introduction
19. Hands-on: Comparing ML Models
20. Tip! - Begin your weekly challenge
21. 1. Defining the Problem - ML Workflow
22. 2. Data Preparation - ML Workflow
23. Data-related Challenges
24. 3. Modeling & related Challenges - ML Workflow
25. Bias & Variance
26. Overfitting & Underfitting
27. Test your Zaka - ML Workflow & Common Challenges
28. Data Methods for Improving ML Models
29. Model Design Methods for Improving ML Models - Vary Complexity
30. Model Design Methods for Improving ML Models - Hyperparameter Tuning
31. Model Design Methods for Improving ML Models - Ensemble Methods
32. Test your Zaka - Improving ML Models - part 1
33. Optimization Methods for Improving ML Models - Regularization
34. Types of Regularizers
35. Test your Zaka - Improving ML Models - part 2
36. Hands-on: Improving ML Models - introduction
37. Hands-on: Improving ML Models
38. Challenge of the Week!
39. Download - Challenge Requirements (Part A)
40. Download - Challenge Requirements (Part B)
41. Download - Week's Run-through
42. Download - Week's Deep Dive
1. Welcome to Week 5!
2. Outline of the week
3. Week Schedule & Important Notes
4. To Do: Complete this Exercise before Wednesday's HCD Workshop
5. Human-centered Design Thinking Workshop - part 2 - Recording Available
6. Solving Backpropagation from SCRATCH Hands-on Workshop - Recording Available
7. Download - Week 5 Slides
8. Intro to Neural Networks & Deep Learning
9. What are Neural Networks?
10. Activation Functions
11. Feedforward Neural Networks
12. Neural Network Hyperparameters
13. How do Neural Networks Learn?
14. Important Note: Do Not Fear the Math!
15. Step 1 - Feed Forward
16. Step 2 - Cost Function
17. Step 3 - Backward Pass - Derivatives of the Final Output Layer
18. Step 3 - Backward Pass - Derivatives of Hidden Layers
19. Step 4 - Weights and Bias Updates
20. Practice & Solve Backpropagation Yourself!
21. Training a Neural Network
22. Intro to Tensorflow Keras
23. Hands On: Build DL Models
24. Evaluating Performance of DL Models
25. Cross Validation
26. Hands On: Pima binary classification
27. Hands On: Cross Validation
28. Data Preparation
29. Hands On: Classification
30. Hands On: Regression
31. Improving DL Models - Data Methods
32. Improving DL Models - Model Design Methods - part 1
33. Improving DL Models - Model Design Methods - part 2
34. Improving DL Models - Optimization Methods - GD Variants
35. Improving DL Models - Optimization Methods - GD Optimization Algorithms
36. Improving DL Models - Optimization Methods - Regularization
37. Improving DL Models - Optimization Methods - Early Stopping
38. Hands On: Advanced Concepts
39. Challenge of the Week!
40. Download - Challenge Requirements
41. Download - Week's Run-through
42. Download - Week's Deep Dive
1. Welcome to Week 6!
2. Outline of the week
3. Week Schedule & Important Notes
4. Download - Week 6 slides
5. Why Do Deep Learning Architectures Differ across Data Types?
6. Part 1 of the Week - Computer Vision
7. What is Computer Vision?
8. Challenges of Computer Vision
9. Image operations
10. Hands-on: Image Operations
11. Convolution operation
12. Hands-on: Image processing
13. Image Data Preparation
14. Hands-on: Image Data Preparation - part 1
15. Hands-on: Image Data Preparation - part 2
16. Image Augmentation
17. Hands-on: Image Data Augmentation
18. Deep Learning for Computer Vision
19. Convolutional Neural Networks (CNN)
20. Pooling layers
21. Hands-on: Convolutions & Pooling - Part 1
22. Hands-on: Convolutions & Pooling - Part 2
23. How to deal with limited data?
24. Transfer Learning & Advanced Architectures
25. Hands-on: Advanced CNN architectures
26. CNNs for Different CV Tasks
27. YOLO (You Only Look Once)
28. Part 2 of the Week - Natural Language Processing
29. Natural Language
30. Challenges of Natural Language
31. NLP applications
32. Text cleaning
33. Hand-on: Text data pre-processing
34. Feature extraction using Bag-of-words
35. Hands-on: Bag-of-words
36. Hands-on: Sentiment Analysis - part 1
37. Hands-on: Sentiment Analysis - part 2
38. Word Embeddings
39. Hands-on: Word Embeddings - Part 1
40. Hands-on: Word Embeddings - Part 2
41. Download - Week's Run-through
42. Download - Week's Deep Dive
1. Welcome to Week 7!
2. Outline of the week
3. Week Schedule & Important Notes
4. An Intro to Reinforcement Learning Workshop - slides available
5. An Intro to Reinforcement Learning Workshop - recording available - part 1
6. An Intro to Reinforcement Learning Workshop - recording available - part 2
7. Download - Week 7 slides
8. Part 1 of the Week - Natural Language Processing
9. Keras' Embedding layer
10. Sequence problems
11. Intro to Recurrent Neural Networks
12. Hands-on: Deep Sentiment Analysis - Part 1
13. Hands-on: Deep Sentiment Analysis - Part 2
14. Test your Zaka
15. Breaking Down Recurrent Neural Networks
16. Variants of RNN Architectures
17. Part 2 of the Week - Time-series Analysis & Prediction
18. What are Time-series?
19. Features of Time-series sequences
20. Test your Zaka
21. Types of Time-series problems
22. Supervised Learning with Time-series Data
23. Test your Zaka
24. Understanding Time-series datasets
25. Hands-on: Case study on power prediction
26. Hands-on: Dataset loading
27. Hands-on: Data inspection
28. Hands-on: Data cleaning - part 1
29. Hands-on: Data cleaning - part 2
30. Hands-on: Data preprocessing
31. Test your Zaka
32. Preparing Time-series datasets
33. Hands-on: Data preparation - part 1
34. Hands-on: Data preparation - part 2
35. Hands-on: Data preparation - part 3
36. Test your Zaka
37. Building Time-series models
38. Hands-on: Model training
39. Test your Zaka
40. Evaluating Time-series models
41. Hands-on: Model evaluation - part 1
42. Hands-on: Model evaluation - part 2
43. Hands-on: Visualizing model performance
44. Test your Zaka
45. Hands-on: Data visualization
46. Download - Week's Run-through
47. Download - Week's Deep Dive
1. Welcome to Week 8!
2. Introduction
3. Outline of the week
4. Week Schedule & Important Notes
5. LIVE: Big Data: Chronicles from the Field - Recording available
6. LIVE: Hands-on Workshop on ML in Production - Recording available
7. Download - Week 8 Slides
8. Phase 1: Planning and Project Setup
9. Test Your Zaka - Planning and Project Setup
10. Phase 2: Data Collection and Labeling
11. Data Collection
12. Data Storage
13. Data Labeling
14. Demo: LabelStudio
15. Data Versioning
16. Data Privacy
17. Recap
18. Test your Zaka - Data Collection and Labeling
19. Phase 3: Model Training and Debugging
20. Writing Code
21. How to use Git?
22. Hands-on: How to use Git
23. Computing Hardware
24. Cloud
25. Linux Commands
26. Model Training
27. Hands-on: Experiment Tracking using MLFlow - part 1
28. Hands-on: Experiment Tracking using MLFlow - part 2
29. Demo: AWS SageMaker - part 1
30. Demo: AWS SageMaker - part 2
31. Model Testing
32. Testing ML Systems
33. Recap
34. Test your Zaka - Model Training and Debugging
35. Phase 4: Model Deploying and Monitoring
36. Deploying / Serving Models
37. Cloud Deployment
38. Hands-on: Deploying ML Models with Flask and Docker
39. Performance Optimization
40. Hands-on: Compressing ML Models
41. Edge Deployment
42. Monitoring Model Performance
43. MLOps
44. Recap
45. Test your Zaka - Model Deploying & Testing
46. Download - Week's Run-through
47. Download - Week's Deep Dive
1. Welcome to Week 9!
2. Week Schedule & Important Notes
3. Monday LIVE - recording available
4. Tuesday LIVE - recording available
5. Wednesday LIVE - recording available
6. Thursday LIVE - recording available
7. Saturday LIVE - recording available
1. Week's Schedule & Important Notes
2. LIVE: Kick-off Session - recording available
3. Action Required: Book your Mentoring Slots
4. Action Required: Book your Mock Interview
5. Tips on Mastering Your Interview
6. Tips on Designing a Killer CV
7. Wednesday Talk - Channeling your Career into Data Science - recording available
8. Thursday Talk - How to Ace your Online Presentations - recording available
9. Friday Talk - Job Search 101: How to Land Interviews without Applying Online - recording available
10. Saturday Talk - Agile... the why!
1. Career Fair Schedule & Final Presentation Expectations
2. GatherTown Platform Demo

About this course

Free
400 lessons
63.5 hours of video content

DELETE Machine Learning Certification

Program curriculum

Welcome to MLC!

Week 0 - Orientation Week

Week 1 - Data Science Foundations

Week 2 - Machine Learning Foundations

Week 3 - Machine Learning Foundations (continued)

Week 4 - Statistical Model Validation & Testing

Week 5 - Neural Networks & Deep Learning

Week 6 - Deep Learning & Advanced Data Types

Week 7 - Deep Learning & Advanced Data Types (continued)

Week 8 - Machine Learning in Production

Week 9 - Machine Learning in Research

Week 10 / 11 - Capstone Projects & Career Fair Prep

Week 12 - Career Fair

About this course