drakearch/kaggle-courses

Kaggle courses and tutorials to get you started in the Data Science world.

kaggle-courses's Issues

Intermediate Machine Learning

Learn to handle missing values, non-numeric values, data leakage and more. Your models will be more accurate and useful.

• Introduction
• Missing Values
• Categorical Variables
• Pipelines
• Cross-Validation
• XGBoost
• Data Leakage

Feature Engineering

• What Is Feature Engineering
• Mutual Information
• Creating Features
• Clustering With K-Means
• Principal Component Analysis
• Target Encoding

Computer Vision

Build convolutional neural networks with TensorFlow and Keras.

• The Convolutional Classifier
• Convolution and ReLU
• Maximum Pooling
• The Sliding Window
• Custom Convnets
• Data Augmentation
• Bonus 1: Create Your First Submission
• Bonus 2: Getting Started: TPUs + Cassava Leaf Disease

Geospatial Analysis

Create interactive maps, and discover patterns in geospatial data

• Your First Map
• Coordinate Reference Systems
• Interactive Maps
• Manipulating Geospatial Data
• Proximity Analysis

Deep Learning

Use TensorFlow to take Machine Learning to the next level. Your new skills will amaze you.

• Intro to DL for Computer Vision
• Building Models From Convolutions
• TensorFlow Programming
• Transfer Learning
• Data Augmentation
• A Deeper Understanding of Deep Learning
• Deep Learning From Scratch
• Dropout and Strides for Larger Models

Intro to Game AI and Reinforcement Learning

Build your own video game bots, using classic algorithms and cutting-edge techniques.

• Play the Game
• One-Step Lookahead
• N-Step Lookahead
• Deep Reinforcement Learning

Advanced SQL

Take your SQL skills to the next level

• JOINs and UNIONs
• Analytic Functions
• Nested and Repeated Data
• Writing Efficient Queries

Intro to SQL

Learn SQL for working with databases, using Google BigQuery to scale to massive datasets.

• Getting Started With SQL and BigQuery
• Select, From & Where
• Group By, Having & Count
• Order By
• As & With
• Joining Data

Python

Review al lessons at Python micro-course from Kaggle.

• Hello, Python
• Functions and Getting Help
• Booleans and Conditionals
• Lists and Tuples
• Loops and List Comprehensions
• Strings and Dictionaries
• Working with External Libraries

Intro to Machine Learning

Learn the core ideas in machine learning and build your first models

• How Models Work
• Basic Data Exploration
• Your First Machine Learning Model
• Model Validation
• Underfitting and Overfitting
• Random Forests
• Exercise: Machine Learning Competitions

Pandas

Short hands-on challenges to perfect your data manipulation skills

• Exercise: Creating, Reading and Writing
• Exercise: Indexing, Selecting & Assigning
• Exercise: Summary Functions and Maps
• Exercise: Grouping and Sorting
• Exercise: Data Types and Missing Values
• Exercise: Renaming and Combining

Time Series

• Linear Regression With Time Series
• Trend
• Seasonality
• Time Series as Features
• Hybrid Models
• Forecasting With Machine Learning

Data Visualization

Make great data visualizations. A great way to see the power of coding!

• Hello, Seaborn
• Line Charts
• Bar Charts and Heatmaps
• Scatter Plots
• Distributions
• Choosing Plot Types and Custom Styles
• Final Project

Data Cleaning

Master efficient workflows for cleaning real-world, messy data.

• Handling Missing Values
• Scaling and Normalization
• Parsing Dates
• Character Encodings
• Inconsistent Data Entry

Machine Learning Explainability

Extract human understandable insights from any Machine Learning model

• Use Cases for Model Insights
• Permutation Importance
• Partial Plots
• SHAP Values
• Advanced Uses of SHAP Values

Natural Language Processing

Distinguish yourself by learning to work with text data.

• Intro to NLP
• Text Classification
• Word Vectors

Feature Engineering

The most effective way to improve your models

• Baseline Model
• Categorical Encodings
• Feature Generation
• Feature Selection

Microchallenges

Ultra-short challenges to build and test your skill

• Blackjack Microchallenge
• Airline Price Optimization Micro-Challenge

Intro to Deep Learning

Use TensorFlow and Keras to build and train neural networks for structured data.

• A Single Neuron
• Deep Neural Networks
• Stochastic Gradient Descent
• Overfitting and Underfitting
• Dropout and Batch Normalization
• Binary Classification
• Bonus 1: Detecting the Higgs Boson With TPUs

numpy version warning.

running the code in Recap section on the kaggle website's exercise, i get the following warning :

nversion} and <{np_maxversion}"

Intro to AI Ethics

Explore practical tools to guide the moral design of AI systems.

• Introduction to AI Ethics
• Human-Centered Design for AI
• Identifying Bias in AI
• AI Fairness
• Model Cards