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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

Excitement going up at Analytics Vidhya (and I can’t stop smiling)!

Kunal Jain
Kunal Jain Last Updated : 04 May, 2015
3 min read

Guess what…we will celebrate our first year anniversary shortly. Last year has just flown by! I still remember the excitement and the anxiety I had while writing my first article on Analytics Vidhya. I had no clue, whether people will read what I’ll write, whether they will like and appreciate it. I didn’t know how things will pan out (I still don’t). What I did know was that I wanted to write and build a community of analysts, where they could learn from each other.

And the year has been nothing short of amazing! Our readers love what we write, they come back to read our new articles time and again. We relish writing articles, doing our research before writing them, listening back from our audience through comments, mails and one on one interactions. We love sleeping with our mind full of new and exciting articles / case studies / tips.anniversary

In our efforts to make Analytics Vidhya more meaningful for our audience, we are taking new (baby) steps. We are very excited about these new initiatives and can’t hide is any more. Following are the new initiatives which have doubled tripled up excitement at Analytics Vidhya:

  • Starting analytics training – Professional training is heart of analytics learning. And the supply for good quality training is limited. So, we couldn’t stop from getting our hands dirty! We will keep a high bar on quality of trainings (similar to all our content) and hope that through these trainings, we will have lot more active role to play in analytics learning of our audience.

The first course which we are launching is a Summer training aimed towards beginners. This course aims to provide deep understanding of basics of analytics. By end of this course, you will be able to build a simple predictive model (in excel). The idea behind this course is not to make you technically proficient in a tool (like most of the trainings out there), but to really drive home the basic fundamentals of statistics and analytics.

So, if you are a fresher looking to enter this industry, a college student passing out in an year or MIS professional looking for a shift to analytics, this could be the course you might be looking for. In order to deliver this training, we have partnered with Internshala. They have tremendous reach among college students and combined we will reach out to the right audience for the course.

If you are interested in this course, we are running an early bird discount offer. The earlier you sign up, the better it will be! You can find more details here.

  • Creating a job alerts platform – Next important aspect for an analyst is to be aware of available career opportunities. In order to help our readers on this, we have launched a job alerts platform. On this platform, you can see some of the best jobs we come across. It is a very basic alerts page to start with. Over time, this will become a single place for looking and searching for analytics jobs (globally). I’ll keep you posted how this develops further.

We have kept job alerts out of the RSS feed / email subscription for now. In next dew days, we will provide a way to sign up for these individually.

In order to keep up with the pace of changes we are driving and make sure we deliver absolute best to our audience, I have decided to devote all my working hours to Analytics Vidhya. Yes, that’s right, over next few weeks, I’ll hand over my responsibilities at my full time employment and become self-employed.

We are absolutely in love with what we are creating for our audience. We hope that each of these initiatives will make Analytics Vidhya more meaningful for you. Do let us know what you think about these new initiatives. What would you want out of them? Any suggestions / feedback / questions? Please feel free to comment / reach out to us, we want to hear more from you.

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.

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.

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Responses From Readers

Clear

shiv
shiv

Hi Kunal, feel good to know about your new venture. 1 Straight Questions : Does it would be helpful to a person with a 4-5 year experience in Digital Marketing who want to switch in Analytic Industry?

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Kunal Jain
Kunal Jain

Shiv, This course would definitely be helpful as a first overview of business analytics. You will go through all the required concepts of statistics, overview of predictive modeling and will be building first predictive model by end of course. Given the nature, it will benefit freshers, college students and MIS professionals in improving their career prospects. Same thing holds for a Digital Marketing person, but at an entry level. If you are looking for a more advanced / experienced position, you should take up a more advanced training. We plan to launch some advanced ones later this year. Regards, Kunal

123
Vishwash Raj Verma
Vishwash Raj Verma

Great Work!!! I would like to help my junior friends by informing them about the great Summer Training Course on Analytics, as they are much interested to learn analytics. Thanks Kunal Jain, I must say they will be benefited by the great new course for the freshers.. Regards, Vishwash

123

Kunal Jain
Kunal Jain

Thanks Vishwash for your support as usual! Regards, Kunal

123
BellurSrikar
BellurSrikar

Hi, I have been following your blog for a few months now. it is very interesting and educational. What you are doing is definitely great service to the population segment hungry for analytics knowledge. Keep up the good work. I live in Washington DC. But would love to meet with you personally my next visit to India. Wish you all the success and the very best in your next venture. Best Regards, Srikar

123

Kunal Jain
Kunal Jain

Thanks Srikar. Look forward to meet you. Let me know when you travel. Regards, Kunal

123

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