Kaggle官方知识体系+课程证书

如果你还在担心以下问题：

• 没有学习资源；
• 没有学习路线；
• 没有学习平台；

那么上述的问题都不是问题，因为Kaggle官方已经提供了丰富的学习资料，具体Kaggle的Courses
页面。

如果你按照要求学习完成，还会获得Kaggle官方认证的证书，是不是非常赞！

课程内容如下，非常推荐入门学习！

Python

Learn the most important language for data science.

课程链接：https://www.kaggle.com/learn/python

课程章节：

• Hello, Python
  : A quick introduction to Python syntax, variable assignment, and numbers.
• Functions and Getting Help
  : Calling functions and defining our own, and using Python's builtin documentation
• Booleans and Conditionals
  : Using booleans for branching logic
• Lists
  : Lists and the things you can do with them. Includes indexing, slicing and mutating
• Loops and List Comprehensions
  : For and while loops, and a much-loved Python feature: list comprehensions
• Strings and Dictionaries
  : Working with strings and dictionaries, two fundamental Python data types
• Working with External Libraries
  : Imports, operator overloading, and survival tips for venturing into the world of external libraries

Intro to Machine Learning

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

课程链接：https://www.kaggle.com/learn/intro-to-machine-learning

• How Models Work
  :The first step if you're new to machine learning.
• Basic Data Exploration
  : Load and understand your data.
• First ML Model
  :Building your first model. Hurray!
• Model Validation
  : Measure the performance of your model ? so you can test and compare alternatives.
• Underfitting and Overfitting
  : Fine-tune your model for better performance.
• Random Forests
  : Using a more sophisticated machine learning algorithm.
• Machine Learning Competitions
  : Enter the world of machine learning competitions to keep improving and see your progress.
• Intro to AutoML
  : Learn how to use automated machine learning (AutoML) to accelerate your work.
• Getting Started With Titanic
  : Create your own Kaggle Notebooks to organize your work in competitions.

Intermediate Machine Learning

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

课程链接：https://www.kaggle.com/learn/intermediate-machine-learning

• Introduction
  : Review what you need for this Micro-Course
• Missing Values
  : Missing values happen. Be prepared for this common challenge in real datasets.
• Categorical Variables
  : There is a lot of non-numeric data out there. Here's how to use it for machine learning.
• Pipelines
  : A critical skill for deploying (and even testing) complex models with pre-processing.
• Cross-Validation
  : A better way to test your models.
• XGBoost
  : The most accurate modeling technique for structured data.
• Data Leakage
  : Find and fix this problem that ruins your model in subtle ways.

Data Visualization

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

课程链接：https://www.kaggle.com/learn/data-visualization

• Hello, Seaborn
  : Your first introduction to coding for data visualization.
• Line Charts
  : Visualize trends over time.
• Bar Charts and Heatmaps
  : Use color or length to compare categories in a dataset.
• Scatter Plots
  : Leverage the coordinate plane to explore relationships between variables.
• Distributions
  : Create histograms and density plots.
• Choosing Plot Types and Custom Styles
  : Customize your charts and make them look snazzy.
• Final Project
  : Practice for real-world application.
• Creating Your Own Notebooks
  : How to put your new skills to use for your next personal or work project.

Pandas

Solve short hands-on challenges to perfect your data manipulation skills.

课程链接：https://www.kaggle.com/learn/pandas

• Creating, Reading and Writing
  : You can't work with data if you can't read it. Get started here.
• Indexing, Selecting & Assigning
  : Pro data scientists do this dozens of times a day. You can, too!
• Summary Functions and Maps
  : Extract insights from your data.
• Grouping and Sorting
  : Scale up your level of insight. The more complex the dataset, the more this matters.
• Data Types and Missing Values
  : Deal with the most common progress-blocking problems.
• Renaming and Combining
  : Data comes in from many sources. Help it all make sense together.

Intro to Deep Learning

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

课程链接：https://www.kaggle.com/learn/intro-to-deep-learning

• A Single Neuron
  : Learn about linear units, the building blocks of deep learning.
• Deep Neural Networks
  : Add hidden layers to your network to uncover complex relationships.
• Stochastic Gradient Descent
  : Use Keras and Tensorflow to train your first neural network.
• Overfitting and Underfitting
  : Improve performance with extra capacity or early stopping.
• Dropout and Batch Normalization
  : Add these special layers to prevent overfitting and stabilize training.
• Binary Classification
  : Apply deep learning to another common task.
• Detecting the Higgs Boson With TPUs
  : Get started with Tensor Processing Units (TPUs)!

Intro to SQL

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

课程链接：https://www.kaggle.com/learn/intro-to-sql

• Getting Started With SQL and BigQuery
  : Learn the workflow for handling big datasets with BigQuery and SQL.
• Select, From & Where
  : The foundational compontents for all SQL queries.
• Group By, Having & Count
  : Get more interesting insights directly from your SQL queries.
• Order By
  : Order your results to focus on the most important data for your use case.
• As & With
  : Organize your query for better readability. This becomes especially important for complex queries.
• Joining Data
  : Combine data sources. Critical for almost all real-world data problems.

Advanced SQL

Take your SQL skills to the next level.

课程链接：https://www.kaggle.com/learn/advanced-sql

• JOINs and UNIONs
  : Combine information from multiple tables. insert_drive_file.
• Analytic Functions
  : Perform complex calculations on groups of rows.
• Nested and Repeated Data
  : Learn to query complex datatypes in BigQuery.
• Writing Efficient Queries
  : Write queries to run faster and use less data.

Data Cleaning

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

课程链接：https://www.kaggle.com/learn/data-cleaning

• Handling Missing Values
  : Drop missing values, or fill them in with an automated workflow.
• Scaling and Normalization
  : Transform numeric variables to have helpful properties.
• Parsing Dates
  : Help Python recognize dates as composed of day, month, and year.
• Character Encodings
  : Avoid UnicoodeDecodeErrors when loading CSV files.
• Inconsistent Data Entry
  : Efficiently fix typos in your data.

Geospatial Analysis

Create interactive maps, and discover patterns in geospatial data.

课程链接：https://www.kaggle.com/learn/geospatial-analysis

• Your First Map
  : Get started with plotting in GeoPandas. insert_drive_file.
• Coordinate Reference Systems
  : It's pretty amazing that we can represent the Earth's surface in 2 dimensions!
• Interactive Maps
  : Learn how to make interactive heatmaps, choropleth maps, and more!
• Manipulating Geospatial Data
  : Find locations with just the name of a place. And, learn how to join data based on spatial relationships.
• Proximity Analysis
  : Measure distance, and explore neighboring points on a map.

Machine Learning Explainability

Extract human-understandable insights from any machine learning model.

课程链接：https://www.kaggle.com/learn/machine-learning-explainability

• Use Cases for Model Insights
  : Why and when do you need insights?
• Permutation Importance
  : What features does your model think are important?
• Partial Plots
  : How does each feature affect your predictions? check.
• SHAP Values
  : Understand individual predictions.
• Advanced Uses of SHAP Values
  : Aggregate SHAP values for even more detailed model insights.

Microchallenges

Solve ultra-short challenges to build and test your skill.

课程链接：https://www.kaggle.com/learn/microchallenges

• Blackjack Microchallenge
  : Test your logic and programming skills by building a better blackjack player.
• Airline Price Optimization Microchallenge
  : Can you improve how airlines set ticket prices?

Feature Engineering

Discover the most effective way to improve your models.

课程链接：https://www.kaggle.com/learn/feature-engineering

• Baseline Model
  : Building a baseline model as a starting point for feature engineering.
• Categorical Encodings
  : There are many ways to encode categorical data for modeling. Some are pretty clever.
• Feature Generation
  : The frequently useful case where you can combine data from multiple rows into useful features.
• Feature Selection
  : You can make a lot of features. Here's how to get the best set of features for your model.

Natural Language Processing

Distinguish yourself by learning to work with text data.

课程链接：https://www.kaggle.com/learn/natural-language-processing

• Intro to NLP
  : Get started with NLP.
• Text Classification
  : Combine machine learning with your newfound NLP skills.
• Word Vectors
  : Explore an idea that ushered in a new generation of NLP techniques.

Intro to Game AI and Reinforcement Learning

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

课程链接：https://www.kaggle.com/learn/intro-to-game-ai-and-reinforcement-learning

• Play the Game
  : Write your first game-playing agent.
• One-Step Lookahead
  : Make your agent smarter with a few simple changes.
• N-Step Lookahead
  : Use the minimax algorithm to dramatically improve your agent.
• Deep Reinforcement Learning
  : Explore advanced techniques for creating intelligent agents.
• Getting Started With Halite
  : Put your new skills to the test with a more complex game.

Computer Vision

Create image classifiers with TensorFlow and Keras, and explore convolutional neural networks.

• The Convolutional Classifier
  : Create your first computer vision model with Keras.
• Convolution and ReLU
  : Discover how convnets create features with convolutional layers.
• Maximum Pooling
  : Learn more about feature extraction with maximum pooling.
• The Sliding Window
  : Explore two important parameters: stride and padding.
• Custom Convnets
  : Design your own convnet.
• Data Augmentation
  : Boost performance by creating extra training data.
• Create Your First Submission
  : Use Kaggle's free TPUs to make a submission to the Petals to the Metal competition!

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