/kaggle-courses

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

Primary LanguageJupyter NotebookMIT LicenseMIT

Kaggle Courses

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.