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 overview
About Zaka
Meet your Zaka support team!
Download your MLC Boarding Kit
Program syllabus
Best practices & tips for success
Are you ready?
Week kick-off
MLC Launch Webinar - Recording Available
Prepwork - Get Ready!
Guidelines to Google Colaboratory's environment
Download - Prepwork cheat sheet
Download - Capstone Project Requirements
Welcome to Week 1!
Outline of the week
Week Schedule & Important Notes
Project Expectations Webinar - Recording available
Download - Week 1 Slides
Data Science Lifecycle
Test your Zaka - DS Lifecycle
EDA Overview
Basic steps of EDA
Test your Zaka - EDA
Data Munging & Wrangling
Test your Zaka - Data Munging
Data Visualization
Case Study: Covid-19 outbreak visualization
Test your Zaka - Data Visualization
Visualizations with Matplotlib
Important Reminder!
Tip! - Refer to Python's documentation
Hands-on: Visualizations with Matplotlib - part 1
Hands-on: Visualizations with Matplotlib - part 2
Hands-on: Visualizations with Matplotlib - part 3
Additional Practice: Visualizations with Seaborn
Data Cleaning
Data Cleaning - Step 1: Diagnosis
Data Cleaning - Step 2: Cleaning
Data Cleaning - Step 3: Finding Correlations
Test your Zaka - Data cleaning
Hands-on: Data Cleaning - Introduction
Hands-on: Data Cleaning - part 1
Hands-on: Data Cleaning - part 2
Hands-on: Data Cleaning - part 3
Hands-on: Data Cleaning - part 4
Tip! - Begin your weekly challenge
Data Manipulation
Test your Zaka - Data Manipulation
Hands-on: Data Manipulation - Introduction
Hands-on: Data Manipulation - part 1
Hands-on: Data Manipulation - part 2
Hands-on: Data Manipulation - part 3
Hands-on: Data Transformation - Introduction
Hands-on: Data Transformation
Feature Engineering
Issues with Features
Test your Zaka - Feature Engineering
Documentation & Reporting
Your Full Data Science Project
Hands-on: Full Data Science Project - Introduction
Hands-on: Full Data Science Project - EDA - part 1
Hands-on: Full Data Science Project - EDA - part 2
Hands-on: Full Data Science Project - EDA - part 3
Hands-on: Full Data Science Project - EDA - part 4
Hands-on: Full Data Science Project - Data Cleaning - part 1
Hands-on: Full Data Science Project - Data Cleaning - part 2
Hands-on: Full Data Science Project - Data Cleaning - part 3
Hands-on: Full Data Science Project - Modeling - part 1
Hands-on: Full Data Science Project - Modeling - part 2
Hands-on: Full Data Science Project - Modeling - part 3
Data Acquisition
Data Types
Test your Zaka - Data Types
Databases
Data Warehouses
Test your Zaka - Databases & Data Warehouses
Challenge of the Week!
Download - Challenge Requirements
Download - Week's Run-through
Download - Week's Deep Dive
Welcome to Week 2!
Outline of the week
Week Schedule & Important Notes
Tuesday-July 13-Office Hours - Recording available
Download - Week 2 Slides
Intro to Machine Learning
Classes of Machine Learning
Types of ML Algorithms
Test your Zaka - Intro to ML
Supervised Learning
Types of Supervised Learning
Supervised Learning Applications
Test your Zaka - Supervised Learning
k-Nearest Neighbors
Test your Zaka - kNN
Linear Regression
Error Functions & Evaluation Metrics for Regression
How to find the best fitting line?
Test your Zaka - Linear Regression
Gradient Descent
Cost Function Derivative
Test your Zaka - Gradient Descent
Hands-on: Linear Regression from Scratch - Introduction
Hands-on: Linear Regression from Scratch - part 1
Hands-on: Linear Regression from Scratch - part 2
Tip! - Begin your weekly challenge
Multi-linear Regression
Logistic Regression
Error Functions for Classification
Test your Zaka - Logistic Regression
Evaluation Metrics for Classifiers
Test your Zaka - Evaluation Metrics for Classifiers
Hands-on: Logistic Regression from Scratch - Introduction
Hands-on: Logistic Regression from Scratch - part 1
Hands-on: Logistic Regression from Scratch - part 2
What do ML models have in common?
Challenge of the Week!
Download - Challenge Requirements
Download - Week's Run-through
Download - Week's Deep Dive
Welcome to Week 3!
Outline of the week
Week Schedule & Important Notes
Download - Week 3 Slides
Naive Bayes
Naive Bayes - Example
Tip! - Solve Examples by Hand
Test Your Zaka: Naive Bayes
Decision Trees
How to Build Decision Trees? - Intuition
Method 1: Information Gain
Method 2: Gini Index
Decision Forests
Test Your Zaka: Decision Trees
Hands-on: Naive Bayes & Decision Trees - Introduction
Hands-on: Naive Bayes & Decision Trees - Data Loading
Hands-on: Naive Bayes Implementation
Hands-on: Decision Trees Implementation
Support Vector Machines (SVMs)
SVM Loss
Non-linear SVM & Kernel Transformations
Test Your Zaka: SVM
Hands-on: SVM - Introduction
Hands-on: SVM - Data Loading
Hands-on: Linear SVM
Hands-on: Non-linear SVM
Intro to Unsupervised Learning
Clustering Methods
K-means Clustering
Test Your Zaka: Clustering Methods
Association Analysis
Association Analysis - Example
Test Your Zaka: Association Analysis
Hands-on Workshop on Unsupervised Learning - part 1 - Recording available
Hands-on Workshop on Unsupervised Learning - part 2 - Recording available
Challenge of the Week!
Download - Challenge Requirements (Part A)
Important Reminder - Solve Part A first!
Download - Challenge Requirements (Part B)
Download - Week's Run-through
Download - Week's Deep Dive
Welcome to Week 4!
Outline of the week
Week Schedule & Important Notes
Human-centered Design Thinking Workshop - part 1 - Recording available
Human-centered Design Thinking Workshop - part 1 - Slides available
Download - Week 4 Slides
Evaluating ML Models
Holdout Method
K-Fold Method
Evaluation Metrics - Review
Test your Zaka - Evaluating ML Models
Hands-on: Evaluating ML Models - introduction
Hands-on: Evaluating ML Models
Comparing ML Models
Z-Score Test
McNemar Test
Test your Zaka - Comparing ML Models
Hands-on: Comparing ML Models - introduction
Hands-on: Comparing ML Models
Tip! - Begin your weekly challenge
1. Defining the Problem - ML Workflow
2. Data Preparation - ML Workflow
Data-related Challenges
3. Modeling & related Challenges - ML Workflow
Bias & Variance
Overfitting & Underfitting
Test your Zaka - ML Workflow & Common Challenges
Data Methods for Improving ML Models
Model Design Methods for Improving ML Models - Vary Complexity
Model Design Methods for Improving ML Models - Hyperparameter Tuning
Model Design Methods for Improving ML Models - Ensemble Methods
Test your Zaka - Improving ML Models - part 1
Optimization Methods for Improving ML Models - Regularization
Types of Regularizers
Test your Zaka - Improving ML Models - part 2
Hands-on: Improving ML Models - introduction
Hands-on: Improving ML Models
Challenge of the Week!
Download - Challenge Requirements (Part A)
Download - Challenge Requirements (Part B)
Download - Week's Run-through
Download - Week's Deep Dive