A repository where I upload all my notebooks of the kaggle courses.

• Intro to Programming
• Python
• Intro to Machine Learning
• Pandas
• Intermediate Machine Learning
• Data Visualization
• Feature Engineering
• Intro to SQL
• Advanced SQL
• Intro to Deep Learning
• Computer Vision
• Time Series
• Data Cleaning
• Intro to AI Ethics
• Geospatial Analysis
• Machine Learning Explainability
• Intro to Game AI and Reinforcement Learning

Get started with Python, if you have no coding experience.

1. Arithmetic and Variables
   Make calculations, and define and modify variables.

2. Functions
   Organize your code and avoid redundancy.

3. Data Types
   Explore integers, floats, booleans, and strings.

4. Conditions and Conditional Statements
   Modify how functions run, depending on the input.

5. Intro to Lists
   Organize your data so you can work with it efficiently.

Bonus lesson: Titanic Tutorial
Make your first submission to a Kaggle competition!

Learn the most important language for data science.

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
   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

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

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

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

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

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

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

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

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

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

Handle missing values, non-numeric values, data leakage, and more.

1. Introduction
   Review what you need for this 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.

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

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

8. Creating Your Own Notebooks
   How to put your new skills to use for your next personal or work project

Better features make better models. Discover how to get the most out of your data.

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.

Bonus lesson: Feature Engineering for House Prices
Apply what you've learned, and join the House Prices competition!

Learn SQL for working with databases, using Google BigQuery.

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

Take your SQL skills to the next level.

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.

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

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.

Build convolutional neural networks with TensorFlow and Keras.

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

2. Convolution and ReLU
   Add hidden layers to your network to uncover complex relationships.

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.

Apply machine learning to real-world forecasting tasks.

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.

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

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

Create interactive maps, and discover patterns in geospatial data.

Extract human-understandable insights from any model.

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

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.