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

About this course

  • Free
  • 400 lessons
  • 63.5 hours of video content