swekshas08 / kaggle-courses

Kaggle-courses

Programming exercise from all Tutorials on Kaggle. You can find all my works here.

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Tutorial 1 - Intro to Programming

01 - Arithmetic and Variables

02 - Functions

03 - Data Types

04 - Conditions and Conditional Statements

05 - Intro to Lists

Tutorial 2 - Python

01 - Syntax Variables and Numbers

02 - Functions and Getting Help

03 - Booleans and Conditionals

04 - Lists

05 - Loops and List Comprehensions

06 - Strings and Dictionaries

07 - Working with external Libraries

Tutorial 3 - Intro to Machine Learning

02 - Explore your data

03 - Your First Machine Learning Model

04 - Model Validation

05 - Underfitting and Overfitting

06 - Random Forests

07 - Machine Learning Competitions

Tutorial 4 - Pandas

01 - Creating, Reading, and Writing

02 - Indexing, Selecting, and Assigning

03 - Summary Functions and Maps

04 - Grouping and Sorting

05 - Data Types and Missing Values

06 - Renaming and Combining

Tutorial 5 - Intermediate to Machine Learning

01 - Introduction

02 - Missing Values

03 - Categorical Variables

04 - Pipelines

05 - Cross-Validation

06 - XGBoost

07 - Data Leakage

Tutorial 6 - Data Visualization

01 - Hello Seaborn

02 - Line Charts

03 - Bar Charts and Heatmaps

04 - Scatter Plots

05 - Distributions

06 - Choosing Ploat Types and Custom Styles

07 - Final Project

Tutorial 7 - Feature Engineering

02 - Mutual Information

03 - Creating Features

04 - Clustering with K-Means

05 - Principal Component Analysis

06 - Target Encoding

Tutorial 8 - SQL

01 - Getting Sstarted with SQL and Bigquery

02 - Select, From & Where

03 - Group By, Having & Count

04 - Order By

05 - As & With

06 - Joining Data

Tutorial 9 - Advanced SQL

01 - JOINs and UNIONs

02 - Analytic Functions

03 - Nested and Repeated Data

04 - Writing Efficient Quries

Tutorial 10 - Introduction to Deep Learning

01 - A Single Neuron

02 - Deep Neural Networks

03 - Stochastic Gradient Descent

04 - Overfitting and Underfitting

05 - Dropout and Batch Normalization

06 - Binary Classification

Tutorial 11 - Computer Vision

01 - The Convolutional Classifier

02 - Convolution and ReLU

03 - Maximum Pooling

04 - The Sliding Window

05 - Custom Convnets

06 - Data Augmentation

Tutorial 12 - Data Cleaning

01 - Handling Missing Values

02 - Scaling and Normalization

03 - Parsing Dates

04 - Character Encodings

05 - Inconsistent data Entry

Tutorial 13 - Time Series

01 - Linear Regression With Time Series

02 - Trend

03 - Seasonality

04 - Time Series as Features

05 - Hybrid Models

06 - Forecasting With Machine Learning

Tutorial 14 - Intro to AI Ethics

02 - Human-Centered Design for AI

03 - Identifying Bias in AI

04 - AI Fairness

05 - Model Cards

Tutorial 15 - Geospatial Analysis

01 - Your First Map

02 - Coordinate Reference Systems

03 - Interactive Maps

04 - Manipulating Geospatial Data

05 - Proximity Analysis

Tutorial 16 - Machine Learning Explainability

02 - Pemutation Importance

03 - Partial Plots

04 - SHAP Values

05 - Advanced Uses of SHAP Values

Tutorial 17 - Intro to Game AI and Reinforcement Learning

01 - Play the Game

02 - One-Step Lookahead

03 - N-Step Lookahead

04 - Deep Reinforcement Learning