wffzxyl/kaggle-courses

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

Kaggle Courses

• Python
• Pandas
• Data Visualization
• Intro to Machine Learning
• Intermediate Machine Learning
• Data Cleaning
• Feature Engineering
• Feature Engineering (2019)
• Geospatial Analysis
• Time Series
• Machine Learning Explainability
• Intro to AI Ethics
• Intro to Deep Learning
• Deep Learning
• Computer Vision
• Natural Language Processing
• Intro to Game AI and Reinforcement Learning
• Intro to SQL
• Advanced SQL
• Microchallenges

Python

1. Hello, Python
   A quick introduction to Python syntax, variable assignment, and numbers.

2. Functions and Getting Help
   Calling functions and defining our own, and using Python's builtin documentation.

3. Booleans and Conditionals
   Using booleans for branching logic.

4. Lists and Tuples
   Lists and the things you can do with them. Includes indexing, slicing and mutating.

5. Loops and List Comprehensions
   For and while loops, and a much-loved Python feature: list comprehensions.

6. Strings and Dictionaries
   Working with strings and dictionaries, two fundamental Python data types.

7. Working with External Libraries
   Imports, operator overloading, and survival tips for venturing into the world of external libraries.

Pandas

1. Exercise: Creating, Reading and Writing
   You can't work with data if you can't read it. Get started here.

2. Exercise: Indexing, Selecting & Assigning
   Pro data scientists do this dozens of times a day. You can, too!

3. Exercise: Summary Functions and Maps
   Extract insights from your data.

4. Exercise: Grouping and Sorting
   Scale up your level of insight. The more complex the dataset, the more this matters.

5. Exercise: Data Types and Missing Values
   Deal with the most common progress-blocking problems.

6. Exercise: Renaming and Combining
   Data comes in from many sources. Help it all make sense together.

Data Visualization

1. Hello, Seaborn
   Your first introduction to coding for data visualization.

2. Line Charts
   Visualize trends over time.

3. Bar Charts and Heatmaps
   Use color or length to compare categories in a dataset.

4. Scatter Plots
   Leverage the coordinate plane to explore relationships between variables.

5. Distributions
   Create histograms and density plots.

6. Choosing Plot Types and Custom Styles
   Customize your charts and make them look snazzy.

7. Final Project
   Practice for real-world application.

Intro to Machine Learning

1. How Models Work
   The first step if you're new to machine learning.

2. Basic Data Exploration
   Load and understand your data.

3. Your First Machine Learning Model
   Building your first model. Hurray!

4. Model Validation
   Measure the performance of your model ? so you can test and compare alternatives.

5. Underfitting and Overfitting
   Fine-tune your model for better performance.

6. Random Forests
   Using a more sophisticated machine learning algorithm.

7. Exercise: Machine Learning Competitions
   Enter the world of machine learning competitions to keep improving and see your progress.

Intermediate Machine Learning

1. Introduction
   Review what you need for this Micro-Course.

2. Missing Values
   Missing values happen. Be prepared for this common challenge in real datasets.

3. Categorical Variables
   There's a lot of non-numeric data out there. Here's how to use it for machine learning.

4. Pipelines
   A critical skill for deploying (and even testing) complex models with pre-processing.

5. Cross-Validation
   A better way to test your models.

6. XGBoost
   The most accurate modeling technique for structured data.

7. Data Leakage
   Find and fix this problem that ruins your model in subtle ways.

Data Cleaning

1. Handling Missing Values
   Drop missing values, or fill them in with an automated workflow.

2. Scaling and Normalization
   Transform numeric variables to have helpful properties.

3. Parsing Dates
   Help Python recognize dates as composed of day, month, and year.

4. Character Encodings
   Avoid UnicodeDecodeErrors when loading CSV files.

5. Inconsistent Data Entry
   Efficiently fix typos in your data.

Feature Engineering

1. What Is Feature Engineering
   Learn the steps and principles of creating better features

2. Mutual Information
   Locate features with the most potential.

3. Creating Features
   Transform features with Pandas to suit your model.

4. Clustering With K-Means
   Untangle complex spatial relationships with cluster labels.

5. Principal Component Analysis
   Discover new features by analyzing variation.

6. Target Encoding
   Boost any categorical feature with this powerful technique.

7. Feature Engineering for House Prices
   Apply what you've learned, and join the House Prices competition!

Feature Engineering (2019)

1. Baseline Model
   Building a baseline model as a starting point for feature engineering.

2. Categorical Encodings
   There are many ways to encode categorical data for modeling. Some are pretty clever.

3. Feature Generation
   The frequently useful case where you can combine data from multiple rows into useful features.

4. Feature Selection
   You can make a lot of features. Here's how to get the best set of features for your model.

Geospatial Analysis

1. Your First Map
   Get started with plotting in GeoPandas.

2. Coordinate Reference Systems
   It's pretty amazing that we can represent the Earth's surface in 2 dimensions!

3. Interactive Maps
   Learn how to make interactive heatmaps, choropleth maps, and more!

4. Manipulating Geospatial Data
   Find locations with just the name of a place. And, learn how to join data based on spatial relationships.

5. Proximity Analysis
   Measure distance, and explore neighboring points on a map.

Time Series

1. Linear Regression With Time Series
   Use two features unique to time series: lags and time steps.

2. Trend
   Model long-term changes with moving averages and the time dummy.

3. Seasonality
   Create indicators and Fourier features to capture periodic change.

4. Time Series as Features
   Predict the future from the past with a lag embedding.

5. Hybrid Models
   Combine the strengths of two forecasters with this powerful technique.

6. Forecasting With Machine Learning
   Apply ML to any forecasting task with these four strategies.

Machine Learning Explainability

1. Use Cases for Model Insights
   Why and when do you need insights?

2. Permutation Importance
   What features does your model think are important?

3. Partial Plots
   How does each feature affect your predictions?

4. SHAP Values
   Understand individual predictions.

5. Advanced Uses of SHAP Values
   Aggregate SHAP values for even more detailed model insights.

Intro to AI Ethics

1. Introduction to AI Ethics
   Learn what to expect from the course.

2. Human-Centered Design for AI
   Design systems that serve people’s needs. Navigate issues in several real-world scenarios.

3. Identifying Bias in AI
   Bias can creep in at any stage in the pipeline. Investigate a simple model that identifies toxic text.

4. AI Fairness
   Learn about four different types of fairness. Assess a toy model trained to judge credit card applications.

5. Model Cards
   Increase transparency by communicating key information about machine learning models.

Intro to Deep Learning

1. A Single Neuron
   Learn about linear units, the building blocks of deep learning.

2. Deep Neural Networks
   Add hidden layers to your network to uncover complex relationships.

3. Stochastic Gradient Descent
   Use Keras and Tensorflow to train your first neural network.

4. Overfitting and Underfitting
   Improve performance with extra capacity or early stopping.

5. Dropout and Batch Normalization
   Add these special layers to prevent overfitting and stabilize training.

6. Binary Classification
   Apply deep learning to another common task.

Deep Learning

1. Intro to DL for Computer Vision
   A quick overview of how models work on images.

2. Building Models From Convolutions
   Scale up from simple building blocks to models with beyond human capabilities.

3. TensorFlow Programming
   Start writing code using TensorFlow and Keras.

4. Transfer Learning
   A powerful technique to build highly accurate models even with limited data.

5. Data Augmentation
   Learn a simple trick that effectively increases amount of data available for model training.

6. A Deeper Understanding of Deep Learning
   How Stochastic Gradient Descent and Back-Propagation train your deep learning model.

7. Deep Learning From Scratch
   Build models without transfer learning. Especially important for uncommon image types.

8. Dropout and Strides for Larger Models
   Make your models faster and reduce overfitting.

Computer Vision

1. The Convolutional Classifier
   Create your first computer vision model with Keras.

2. Convolution and ReLU
   Discover how convnets create features with convolutional layers.

3. Maximum Pooling
   Learn more about feature extraction with maximum pooling.

4. The Sliding Window
   Explore two important parameters: stride and padding.

5. Custom Convnets
   Design your own convnet.

6. Data Augmentation
   Boost performance by creating extra training data.

7. Create Your First Submission
   Use Kaggle's free TPUs to make a submission to the Petals to the Metal competition!

8. Getting Started: TPUs + Cassava Leaf Disease
   Use Kaggle's free TPUs to make a submission to the Cassava Leaf Disease Classification competition.

Natural Language Processing

1. Intro to NLP
   Get started with NLP.

2. Text Classification
   Combine machine learning with your newfound NLP skills.

3. Word Vectors
   Explore an idea that ushered in a new generation of NLP techniques.

Intro to Game AI and Reinforcement Learning

1. Play the Game
   Write your first game-playing agent.

2. One-Step Lookahead
   Make your agent smarter with a few simple changes.

3. N-Step Lookahead
   Use the minimax algorithm to dramatically improve your agent.

4. Deep Reinforcement Learning
   Explore advanced techniques for creating intelligent agents.

Intro to SQL

1. Getting Started With SQL and BigQuery
   Learn the workflow for handling big datasets with BigQuery and SQL.

2. Select, From & Where
   The foundational compontents for all SQL queries.

3. Group By, Having & Count
   Get more interesting insights directly from your SQL queries.

4. Order By
   Order your results to focus on the most important data for your use case.

5. As & With
   Organize your query for better readability. This becomes especially important for complex queries.

6. Joining Data
   Combine data sources. Critical for almost all real-world data problems.

Advanced SQL

1. JOINs and UNIONs
   Combine information from multiple tables.

2. Analytic Functions
   Perform complex calculations on groups of rows.

3. Nested and Repeated Data
   Learn to query complex datatypes in BigQuery.

4. Writing Efficient Queries
   Write queries to run faster and use less data.

Microchallenges

1. Blackjack Microchallenge
   Test your logic and programming skills with by building a better BlackJack player.

2. Airline Price Optimization Micro-Challenge
   Can you set the best airfare prices in our Airline Sales simulator.