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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

Independence Day Bonanza with Analytics Vidhya’s Offers and Launches!

Pranav Dar
Pranav Dar Last Updated : 07 May, 2019
3 min read

Introduction

India is celebrating its 72nd Independence Day and so are we! It’s day to celebrate our past, enjoy the present and be optimistic about the future.

What would be the best way to do this? The best way is to build things for future and enable more and more people to do so. That’s what we will be doing this Independence Day.

Let me tell what is in store.

 

Podcast with Dr. Avik Sarkar, Head of Data Science Cell, NITI Aayog

Dr. Avik Sarkar is the Head of the Data Science Cell as part of the Indian government’s NITI Aayog initiative. He is involved in the effective use of artificial intelligence, big data and data science techniques for social good in areas of real-time governance, healthcare, agriculture monitoring, education, energy, etc.

This exclusive podcast will feature Kunal in conversation with Dr. Avik about the state of the data science field in India, what and how NITI Aayog is using data science to work on various challenges in multiple Indian sectors, among other things.

This podcast will be released on the evening of August 14th on all the below platforms:

Happy listening!

 

60% off on all training courses!!

We will offer incredible deals on our existing as well as upcoming training courses . It all kicks off on 11th August midnight. Check them out.

Whether you want to learn data science, machine learning, computer vision, MS Excel, or want to understand what Artificial Intelligence is from a business leader’s perspective, we are offering each of these courses at flat 60% off. An unmissable offer!

Simply use the code IDAY60 when you checkout to avail this offer on all the below courses:

Introduction to Data Science

Computer Vision using Deep Learning

AI and ML for Business Leaders

Microsoft Excel: Beginners to Advanced

 

Flat Rs. 1,000 off on DataHack Summit 2018 tickets!

Analytics Vidhya’s flagship event DataHack Summit 2018 is one of the most anticipated and eagerly awaited machine learning, deep learning artificial intelligence and IoT conferences this year. It will be held on November 22-24 in Bengaluru.

To get flat Rs. 1,000 off on DHS 2018 tickets, use the code IDAY1000.

 

Resources and Ideas on how Data can help us solve problems in India

  • How can data science power the agriculture industry?
  • How smart cities projects is evolving, you might think the use data in smart city project?
  • Knowing the diversity, how data can help to define and deploy policies to uplift the social strength?
  • How are we taking help of recent technologies(AI) to improve the healthcare services?
  • Or how about the electricity and water sector?

These are just some of the sectors we will be exploring in our Independence Day article (to be published on 14th November). Data science is a powerful tool so why shouldn’t we use it for the betterment of this wonderful nation?

We will provide links to resources like open datasets which you can download and contribute back to the community. It’s both an informational piece as well as a call to action. All aboard the ‘Data Science for good’ express!

 

Launch of AV Editor’s Club

Analytics Vidhya is known for its content and the kind of effort we put in creating that. We want to democratise this and get more and more community members to learn the way we work. That is why, we are launching AV Editor’s Club. Here are a few benefits you will get as a member of this club:

  • You will be an Editor of Analytics Vidhya – so you get some limelight 🙂
  • You get first hand experience of running and building a community
  • You get to learn, read, write and edit a lot. We really mean a lot
  • Networking with a group of people with similar passion

Stay tuned for more details. We will announce this on 14th August.

 

Excited? We are as well. Enjoy Analytics Vidhya’s offerings and once again, a very happy Independence Day to our community!

Pranav Dar
Pranav Dar

Senior Editor at Analytics Vidhya.Data visualization practitioner who loves reading and delving deeper into the data science and machine learning arts. Always looking for new ways to improve processes using ML and AI.

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