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https://github.com/aj1904/kaggle-certifications


https://github.com/aj1904/kaggle-certifications

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README 

          
# Kaggle certifications

This repository contains the notebooks used to complete the Kaggle certifications.



# Advanced SQL

* Took SQL skills to the next level.

* Instructor: Alexis Cook

* Link to [Certificate](https://www.kaggle.com/learn/certification/ayushrijain/advanced-sql)

## Lessons

### 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.



# Computer Vision

* Built convolutional neural networks with TensorFlow and Keras.

* Instructor: Ryan Holbrook

* Link to [Certificate](https://www.kaggle.com/learn/certification/ayushrijain/computer-vision)

## Lessons

### 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.



# Data Cleaning

* Mastered efficient workflows for cleaning real-world, messy data.

* Instructor: Rachael Tatman

* Link to [Certificate](https://www.kaggle.com/learn/certification/ayushrijain/data-cleaning)

## Lessons

### 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 UnicoodeDecodeErrors when loading CSV files.

### 5. Inconsistent Data Entry

Efficiently fix typos in your data.



# Data Visualization

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

* Instructor: Alexis Cook

* Link to [Certificate](https://www.kaggle.com/learn/certification/ayushrijain/data-visualization)

## Lessons

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



# Feature Engineering

* Better features make better models. Discovered how to get the most out of the data.

* Instructor: Ryan Holbrook

* Link to [Certificate](https://www.kaggle.com/learn/certification/ayushrijain/feature-engineering)

## Lessons

### 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.



# Geospatial Analysis

* Created interactive maps, and discover patterns in geospatial data.

* Instructor: Alexis Cook, Jessica Li

* Link to [Certificate](https://www.kaggle.com/learn/certification/ayushrijain/geospatial-analysis)

## Lessons

### 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.



# Intermediate Machine Learning

* Handled missing values, non-numeric values, data leakage, and more.

* Instructor: Alexis Cook

* Link to [Certificate](https://www.kaggle.com/learn/certification/ayushrijain/intermediate-machine-learning)

## Lessons

### 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.



# Intro to AI Ethics

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

* Instructor: Alexis Cook, Var Shankar

* Link to [Certificate](https://www.kaggle.com/learn/certification/ayushrijain/intro-to-ai-ethics)

## Lessons

### 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

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

* Instructor: Ryan Holbrook

* Link to [Certificate](https://www.kaggle.com/learn/certification/ayushrijain/intro-to-deep-learning)

## Lessons

### 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.



# Intro to Game AI and Reinforcement Learning

* Built video game bots, using classic and cutting-edge algorithms.

* Instructor: Alexis Cook

* Link to [Certificate](https://www.kaggle.com/learn/certification/ayushrijain/intro-to-game-ai-and-reinforcement-learning)

## Lessons

### 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 Machine Learning

* Learned the core ideas in machine learning, and build models.

* Instructor: Dan Becker

* Link to [Certificate](https://www.kaggle.com/learn/certification/ayushrijain/intro-to-machine-learning)

## Lessons

### 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.



# Intro to Programming

* Preparation for Python.

* Instructor: Alexis Cook

* Link to [Certificate](https://www.kaggle.com/learn/certification/ayushrijain/intro-to-programming)

## Lessons

### 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.



# Intro to SQL

* Learned SQL for working with databases, using Google BigQuery.

* Instructors: Rachael Tatman, Alexis Cook

* Link to [Certificate](https://www.kaggle.com/learn/certification/ayushrijain/intro-to-sql)

## Lessons

### 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



# Machine Learning Explainability

* Extract human-understandable insights from any model.

* Instructor: Dan Becker

* Link to [Certificate](https://www.kaggle.com/learn/certification/ayushrijain/machine-learning-explainability)

## Lessons

### 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



# Pandas

* Solved short hands-on challenges to perfect data manipulation skills.

* Instructor: Aleksey Bilogur

* Link to [Certificate](https://www.kaggle.com/learn/certification/ayushrijain/pandas)

## Lessons

### 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



# Python

* Learned the most important language for data science.

* Instructor: Colin Morris

* Link to [Certificate](https://www.kaggle.com/learn/certification/ayushrijain/python)

## Lessons

### 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



# Time Series

* Applied machine learning to real-world forecasting tasks.

* Instructor: Ryan Holbrook

* Link to [Certificate](https://www.kaggle.com/learn/certification/ayushrijain/time-series)

## Lessons

### 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.