Kaggle Courses
Lecture notes and programming exercise from all Tutorials on Kaggle. You can find all my works here.
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Tutorial 1 - Intro to Programming
Lecture Notes
- 01 - Arithmetic and Variables
- 02 - Functions
- 03 - Data Types
- 04 - Conditions and Conditional Statements
- 05 - Intro to Lists
Exercise
- 01 - Arithmetic and Variables
- 02 - Functions
- 03 - Data Types
- 04 - Conditions and Conditional Statements
- 05 - Intro to Lists
Tutorial 2 - Python
Lecture Notes
- 01 - Hello Python
- 02 - Functions and Getting Help
- 03 - Booleans and Conditionals
- 04 - Lists
- 05 - Loops and List Comprehensions
- 06 - Strings and Dictionaries
Exercise
- 01 - Syntax Variables and Numbers
- 02 - Functions and Getting Help
- 03 - Booleans and Conditionals
- 04 - Lists
- 05 - Loops and List Comprehensions
- 06 - Strings and Dictionaries
- 07 - Working with external Libraries
Tutorial 3 - Intro to Machine Learning
Lecture Notes
- 01 - How Models Work
- 02 - Basic Data Exploration
- 03 - Your First Machine Learning Model
- 04 - Model Validation
- 05 - Underfitting and Overfitting
- 06 - Random Forests
- 07 - Machine Learning Competitions
Exercise
- 02 - Explore your data
- 03 - Your First Machine Learning Model
- 04 - Model Validation
- 05 - Underfitting and Overfitting
- 06 - Random Forests
- 07 - Machine Learning Competitions
Tutorial 4 - Pandas
Lecture Notes
- 01 - Creating, Reading, and Writing
- 02 - Indexing, Selecting, and Assigning
- 03 - Summary Functions and Maps
- 04 - Grouping and Sorting
- 05 - Data Types and Missing Values
- 06 - Renaming and Combining
Exercise
- 01 - Creating, Reading, and Writing
- 02 - Indexing, Selecting, and Assigning
- 03 - Summary Functions and Maps
- 04 - Grouping and Sorting
- 05 - Data Types and Missing Values
- 06 - Renaming and Combining
Tutorial 5 - Intermediate to Machine Learning
Lecture Notes
- 01 - Introduction
- 02 - Missing Values
- 03 - Categorical Variables
- 04 - Pipelines
- 05 - Cross-Validation
- 06 - XGBoost
- 07 - Data Leakage
Exercise
- 01 - Introduction
- 02 - Missing Values
- 03 - Categorical Variables
- 04 - Pipelines
- 05 - Cross-Validation
- 06 - XGBoost
- 07 - Data Leakage
Tutorial 6 - Data Visualization
Lecture Notes
- 01 - Hello Seaborn
- 02 - Line Charts
- 03 - Bar Charts and Heatmaps
- 04 - Scatter Plots
- 05 - Distributions
- 06 - Choosing Ploat Types and Custom Styles
- 07 - Final Project
- 08 - Creating Your Own Notebook
Exercise
- 01 - Hello Seaborn
- 02 - Line Charts
- 03 - Bar Charts and Heatmaps
- 04 - Scatter Plots
- 05 - Distributions
- 06 - Choosing Ploat Types and Custom Styles
- 07 - Final Project
Tutorial 7 - Feature Engineering
Lecture Notes
- 01 - What is Feature Engineering
- 02 - Mutual Information
- 03 - Creating Features
- 04 - Clustering with K-Means
- 05 - Principal Component Analysis
- 06 - Target Encoding
- 07 - Feature Engineering for house prices
Exercise
- 02 - Mutual Information
- 03 - Creating Features
- 04 - Clustering with K-Means
- 05 - Principal Component Analysis
- 06 - Target Encoding
Tutorial 8 - SQL
Lecture Notes
- 01 - Getting Sstarted with SQL and Bigquery
- 02 - Select, From & Where
- 03 - Group By, Having & Count
- 04 - Order By
- 05 - As & With
- 06 - Joining Data
Exercise
- 01 - Getting Sstarted with SQL and Bigquery
- 02 - Select, From & Where
- 03 - Group By, Having & Count
- 04 - Order By
- 05 - As & With
- 06 - Joining Data
Tutorial 9 - Advanced SQL
Lecture Notes
- 01 - JOINs and UNIONs
- 02 - Analytic Functions
- 03 - Nested and Repeated Data
- 04 - Writing Efficient Quries
Exercise
- 01 - JOINs and UNIONs
- 02 - Analytic Functions
- 03 - Nested and Repeated Data
- 04 - Writing Efficient Quries
Tutorial 10 - Introduction to Deep Learning
Lecture Notes
- 01 - A Single Neuron
- 02 - Deep Neural Networks
- 03 - Stochastic Gradient Descent
- 04 - Overfitting and Underfitting
- 05 - Dropout and Batch Normalization
- 06 - Binary Classification
Exercise
- 01 - A Single Neuron
- 02 - Deep Neural Networks
- 03 - Stochastic Gradient Descent
- 04 - Overfitting and Underfitting
- 05 - Dropout and Batch Normalization
- 06 - Binary Classification
Tutorial 11 - Computer Vision
Lecture Notes
- 01 - The Convolutional Classifier
- 02 - Convolution and ReLU
- 03 - Maximum Pooling
- 04 - The Sliding Window
- 05 - Custom Convnets
- 06 - Data Augmentation
Exercise
- 01 - The Convolutional Classifier
- 02 - Convolution and ReLU
- 03 - Maximum Pooling
- 04 - The Sliding Window
- 05 - Custom Convnets
- 06 - Data Augmentation
Tutorial 12 - Data Cleaning
Lecture Notes
- 01 - Handling Missing Values
- 02 - Scaling and Normalization
- 03 - Parsing Dates
- 04 - Character Encodings
- 05 - Inconsistent data Entry
Exercise
- 01 - Handling Missing Values
- 02 - Scaling and Normalization
- 03 - Parsing Dates
- 04 - Character Encodings
- 05 - Inconsistent data Entry
Tutorial 13 - Time Series
Lecture Notes
- 01 - Linear Regression With Time Series
- 02 - Trend
- 03 - Seasonality
- 04 - Time Series as Features
- 05 - Hybrid Models
- 06 - Forecasting With Machine Learning
Exercise
- 01 - Linear Regression With Time Series
- 02 - Trend
- 03 - Seasonality
- 04 - Time Series as Features
- 05 - Hybrid Models
- 06 - Forecasting With Machine Learning
Tutorial 14 - Intro to AI Ethics
Lecture Notes
- 01 - Introduction to AI Ethics
- 02 - Human-Centered Design for AI
- 03 - Identifying Bias in AI
- 04 - AI Fairness
- 05 - Model Cards
Exercise
Tutorial 15 - Geospatial Analysis
Lecture Notes
- 01 - Your First Map
- 02 - Coordinate Reference Systems
- 03 - Interactive Maps
- 04 - Manipulating Geospatial Data
- 05 - Proximity Analysis
Exercise
- 01 - Your First Map
- 02 - Coordinate Reference Systems
- 03 - Interactive Maps
- 04 - Manipulating Geospatial Data
- 05 - Proximity Analysis
Tutorial 16 - Machine Learning Explainability
Lecture Notes
- 01 - Use Cases for Model Insights
- 02 - Pemutation Importance
- 03 - Partial Plots
- 04 - SHAP Values
- 05 - Advanced Uses of SHAP Values