Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

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

Exciting updates from Analytics Vidhya – Launch of Trainings and DataHack platform update!

Kunal Jain
Kunal Jain Last Updated : 10 May, 2019
4 min read

Introduction

A lot has changed at Analytics Vidhya over last few years.

We transformed from a blog into a community portal. Our small office is buzzing with activities throughout the day (and night). We delivered India’s largest data science conference – DataHack Summit 2017 and are making it bigger this year.

But, one thing has stayed same throughout our journey. This is our commitment to provide world class learning resources to our community. If there is one thing which defines Analytics Vidhya – it is the amount of learning we enable for our community and the impact we have created in their professional careers.

Today, I am excited to announce a couple of updates which will take this learning to the next level.

 

Launch of Analytics Vidhya training marketplace

Given our focus on enabling learning and our ability to create / differentiate high quality content – building a trainings platform was only a matter of time.

 

Why create this marketplace?

There is no dearth of trainings available today. But, when it comes to high quality trainings – there are only a few of them available. This is why we decided to create a marketplace where each course is vetted by our standard of creating content. We understand how to enable learning and we will use our knowledge and experience to make sure that every course in this marketplace is vetted for quality.

 

What can you expect from our trainings marketplace?

We plan to offer a mix of free and paid training courses. Some of these courses will be created by Analytics Vidhya, others by our community members and partners. Every course in our market place will be vetted for quality by Analytics Vidhya.

Each course will cover multiple real life industry problems and would enable you to become better in data science.

If any course falls below our quality threshold – we will not launch it.

 

Free vs. Paid Courses:

There are a few differences between our Free and Paid courses. Here is a brief summary of them:

  • Free courses come with community support where as paid courses come with support of instructors / teaching assistants.
  • There are no certificates available with free courses. Paid courses come with certificates, which you can generate online once you have finished the course satisfactorily. Only people who complete the course satisfactorily will get the certificates – as we want these certificates to hold a value.

 

Current Courses in Trainings Marketplace:

There are a range of training courses available on the marketplace today – some free, some paid. All our courses today have an average rating of 4.5 or above on a scale of 1 – 5.

Here is the current spread of courses:

Free Courses:

Paid Courses:

Invite only courses:

  • DataHack Summit – Day 1
  • DataHack Summit – Day 2

All the paid courses are running under introductory pricing for the next 10 days. So, if you plan to buy one – make sure you buy them before end of this month.

 

Updates to the DataHack platform

In addition to the launch of trainings, we have made a few changes in our DataHack platform. The idea is to help you learn more from each of the competitions we host.

Here are the changes in our DataHack platform:

  1. Participants who registered for a hackathon can now download datasets and check their scores even after competitions – These hackathons will be available for you all year round, with no deadlines. The idea is to enable you to learn as much as you can. You can now continue to fine tune your models, replicate winner’s solutions and upload your results. You can check your scores on the competitions by uploading your solutions. Obviously, the leaderboard will not update on these solutions.
  2. People can share their approach through the platform – If you have participated in a hackathon, you would know the value of this. Going forward, every participant can share their approach with the rest of the community. This approach could be an article, link to your GitHub repo, a video explaining what you did. Just go to the leaderboard and update your solution and the rest of the community can see it. Sharing and collaboration are at the heart of what we do and this is another step towards solidifying that idea

 

Get Started!

Excited? We are excited as well. Looking forward to hear from you on what you think about these changes. Also, if you have any suggestions / feedback – let us know.

Till then – Happy Learning!

data science coursesdatahackExcel
Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.

Free Courses

Generative AI 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
Large Language Models 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
Prompt Engineering 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
LlamaIndex 4.8

Improving Real World RAG Systems: Key Challenges & Practical Solutions

Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
RAG systems using LlamaIndex 4.7

Microsoft Excel: Formulas & Functions

Master MS Excel for data analysis with key formulas, functions, and LookUp tools in this comprehensive course.

Recommended Articles

Responses From Readers

Clear

Harsha
Harsha

Very impressive step on sharing solution post the hackathon. This was one of the hindrance many people were looking for during past many competition events.

123

Marshall J Keyes, MD
Marshall J Keyes, MD

Hi Folks, I am a fan of Analytics Vidhya and have received your emails for a number of years. If you can provide, in English, either small classes or individual tutoring in Deep Leaning (DL), Reinforcement Learning (RL), Evolution Strategies (ES), I would be interested in spending time in India to take such classes/tutoring. BTW, I have traveled to India many times and lived there for a summer. My major interest in in creating a prediction system for non-stationary time series data as is found in physiologic, finance/stock market and weather data. I am also interested in the possibility of using Alpha Zero and/or OpenAI to develop the system. Please let me know if you can provide such services. If not, perhaps you can refer me to someone who can. BTW, although I do not program, I have a reasonably good understanding of the underlying principles of Neural Networks (NN) and DL. I am a retired American physician. I would need someone whose English is good and will teach using Python, Tensorflow, Keras, etc. but not R or other languages. Your prompt response is appreciated. Thanks, Marshall

123

Rich
Rich

How does one use the DataHack and Hackathon site? For a newbie there is no easily found instruction to give guidance on how to use this to learn datascience.

123

Aishwarya Singh
Aishwarya Singh

Hi Rich, You can login(or sign up) on the datahack platform

123
Aishwarya Singh
Aishwarya Singh

Hi, You can sign up on the datahack. In order to participate for a competition, click on 'participate' and register yourself. Otherwise, you can also register yourself for the practice problems

123

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag
Congratulations, You Did It!
Well Done on Completing Your Learning Journey. Stay curious and keep exploring!
Nikhil Mishra’s Journey to Becoming a Kaggle Grandmaster
Nikhil Mishra’s Journey to Becoming a Kaggle Grandmaster
[Announcement] Launching Analytics Vidhya Certified Programs and new Courses portal
[Announcement] Launching Analytics Vidhya Certified Programs and new Courses portal

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details