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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

The First Look and Getting started with Kaggle

Gargeya
Gargeya Last Updated : 08 Jun, 2021
6 min read

This article was published as a part of the Data Science Blogathon

Introduction

Every career today needs to have a community, a group of people with whom we can talk about work, errors, ideas, and learn. Kaggle is the most popular and world’s largest data science community. Having such a community helps us feel “we belong” which is one of the crucial feelings for our social interaction and health.

In this article, we will be looking at Kaggle as a whole community and Kaggle as a Platform:  all its different tools, services, and resources available for us to learn as well as practice data science.

Let’s look at the interface that we get when we visit Kaggle for the first time.

kaggle interface

Before we start using Kaggle, we need to create an account and then sign in, you can see both the options on the top right corner. Once you are done with that, this is what It might look like.

kaggle create account

Some of the things visible here might be different for you because the interface is personalized with how I have used Kaggle till now from the time I registered.

Navbar and all the things available to us on Kaggle:

 

kaggle navbar

Once I click on ‘more’, that’s all the things I can access from my Kaggle account.

exploring more

In my opinion, there are 4 major things that make Kaggle “THE BEST”.

1. Free Courses and free certificates available

 There are so many courses available on multiple domains of machine learning and Data Science. Not only the courses are available, after each lesson, but there are also practice (exercise) notebooks available to get hands-on on the topic. For your free Kaggle certificate, completion of all the tasks and exercises is necessary.

kaggle courses and certificates
courses
courses 2

Few more courses are there, but through this, I wanted to show you that there exists such diversity of topics in these courses that you don’t have to go anywhere, anytime to feel lost on some topic or problem get help from here.

Let me show you how, how these courses look like from one example:

kaggle | intro to ml
intro to ml 2

At the end of each course, there is one additional lesson, which is different in terms of content but similar to the use case and understanding of the course. Mostly they include some famous or/and powerful topic. Here we have, AutoML (by Google) to automate Machine learning.

2. A Huge collection of publicly available/ contributed datasets to practice/ work on

For any data science or Machine Learning or Deep learning tasks, we need data, and a whole lot of it most of the time. Instead of browsing on different sites for different kind/ size of the dataset, Kaggle provides a common place for a huge collection of all these datasets. You can one click away from using them. They are extremely easy to use.

datasets

 

kaggle more datasets

Once you click on “Datasets” on the Navbar, this is what you will see. You can search for some specific dataset, import/ contribute your own dataset into the community or study or start working on a dataset, shown on this page. (Trending Datasets, Popular Datasets, Recently Viewed Datasets)

For demonstration, I will be searching for a specific dataset (“Sunspots dataset”). Let us see how it looks like.

demonstration

The number in red selection is the number of upvote people gave it, for the best relevant/liked option. Let’s explore and look at this dataset in detail.

There are a lot of things that we can use to know more about this data and immediately start working.

  • You can download the dataset,
  • create a new Kaggle Notebook with this dataset already loaded in it.
  • Few certain details about columns inside the data.
  • Activities that involve this data.
  • Last but not the least, all the notebooks created and publicly shared till this date that uses this data.
kaggle sunspots dataset
data explorer
activity kaggle
code

3. Data Science/ Machine Learning / Deep learning Competitions

Although I haven’t participated in any of them, I love how we complete in real-time on a problem along with the Kaggle community and win amazing cash prizes (if involved in that particular competition). I definitely wanna participate someday soon, I hope the images motivate you. It’s not necessary that only big companies or wealthy enterprises can do that. You can do that as well. There are certain protocols that need to be followed and voila, you have your own hosted competition.

kaggle competitions
all competitions

I have sorted the completed competition till today on their reward value. Look closely.

4. Kaggle Notebooks (Code)

For any Data Science or Computer Science related task, we have to write at least some code. Kaggle provides us with its own Notebook environment with a certain limit to how much we can store on them (collectively per account), how many hours of GPU available, and How many hours of TPU available. They are completely integrated with all Kaggle’s services and can be used independently like any other notebook environment (Datalore, Google Colab, Jupyter, etc), which means, you can use them for your practice, Kaggle competitions,  Kaggle courses, analyzing some Kaggle/ or non-Kaggle dataset and many more. You must check them out.

kaggle notebook

Clicking on that black button you create your notebook or open someone else’s notebook that you wanna read and learn from/ compare from. All these visible notebooks are explicitly publicly shared which means your notebooks won’t be visible to anyone unless you chose to do so.

 

To switch from CPU to GPU or TPU, follow this:

switch to GPU and TPU

 

These are most of the functional options that are available to you regarding this notebook.:

kaggle notebook more functions
kaggle notebook2

 

Let us see how to use them with data (imported/ taken directly from Kaggle/ downloaded from URL etc) and start working on your data science tasks.

kaggle add data
add data kaggle

Here I will show you how to use that “Sunspots” dataset that we saw earlier. Start by searching.

use dataset
added data

 

Now the data is successfully loaded. The selection on the image above is the directory in which it is stored. Let us see a small pandas code for how to import the dataset.

kaggle pandas code

 

The last thing that you can do, after you complete your project/ work is to share it with the community on Kaggle. This is an important step because through sharing our ideas, our work we expand the utilities available to the community and support each other. We grow because of each other.

Left to the Big blue button in the top right, you will see a “share” button. Click on that and select Public from the dropdown menu.

kaggle share

 

I hope you liked what you saw in this guide, and feel eager to start using Kaggle.

Gargeya Sharma

B.Tech Computer Science 3rd year
Specialized in Data Science and Deep Learning
Data Scientist Intern at Upswing Cognitive Hospitality Solutions
For more info check out my Github Home Page

LinkedIn                GitHub

The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion.

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