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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

Launch of AV Casino – An Introduction to Probability

Kunal Jain
Kunal Jain Last Updated : 15 Sep, 2016
3 min read

Over the last few months, you would have seen us experimenting with various formats to aid learning and knowledge exchange among our community members. These formats not only include the blog and the discussion portals, but also the ML competitions, Strategic thinking hackathons, workshops and interactive courses. The idea has been to try various formats and understand what helps our community learn analytics in the best possible manner. While we will continue to try some more interesting formats in the coming months, I wanted to talk specifically about our workshops AV Casino – Introduction to Probability and AV Casino – Introduction to Prbability 2  today.

Cover Casino

 

Why we designed the AV Casino?

Here is an interesting conflict happening in the industry. If you talk to any experienced person in the industry with 5 or more years of experience – they will quote Probability and Statistics as one of the most critical skills for a career in Analytics and Data Science. Here is what Mr. Srikanth Velamakkani, CEO – Fractal Analytics had to say in an exclusive interview we did with them in past:

I recommend analytics aspirants to develop a good understanding of probability and see if they enjoy it

– Mr. Srikanth Velamakkani, CEO, Fractal Analytics

On the other hand, I have met a lot of people early in their analytics career, who seem to think otherwise. A large chunk of people coming from software background tend to pick up machine learning as just an extension to a software code. They tend to skip the basic mathematical and statistical concepts and go on to simply write the code for machine learning algorithms without necessarily understanding the fundamentals. While it might help them in the short term, it definitely comes up as a gap in learning over long term.

The idea behind creating AV Casino was to bring some fun in learning statistics and probability – so that it helps a larger audience. We could have easily written a few articles to explain people about basics of probability. While this would have given us good reach, it would not have been as effective and as much fun as we would have wanted it to be. Hence, we designed AV Casino I and AV Casino II !

Register Here – AV Casino I

Once you have taken the first workshop to go ahead to experience the next level.

Register Here – AV Casino II

 

Benefits of learning probability through AV Casino:

Probability is one of the most important concepts to learn, if you are serious about a career in Analytics. And learning it in a fun interactive manner would make it even better!

Here are a few things we kept in mind while creating this workshop:

  1. We wanted to provide bite sized learning to our audience so that you do not get overwhelmed by the mathematical concepts.
  2. Bringing an interaction based learning so that you are applying the concepts while you are learning them.
  3. Providing a structured framework for people undergoing this learning rather than covering this in multiple articles, which might be difficult to track among other articles.

And guess what, we think this is just the start of an interesting series. In future, we plan to do series of workshops as an extension to this workshop, which would include topics like Conditional Probability, Hypothesis Testing, Markov chains etc.

 

Agenda of AV Casino

Without stealing too much of thunder from the workshop, here is the agenda of the workshop:

  1. Getting ready for the AV Casino
  2. Basics of Probability
  3. Addition and multiplication rule
  4. Over-counting and Inclusion-Exclusion principle
  5. Complements, Bose-Einstein, Inclusion-Exclusion
  6. Main Villains
  7. End Notes

As you can see, the agenda starts from basics, builds up slowly and ends up with some of the most thought provoking problems in probability. I don’t want to sound like I am boasting, but I have not seen such lucid content on this topic elsewhere!

 

Looking forward to your feedback:

As usual, whenever we launch something, we look out to hear from you. That is the only way we learn how our content and tutorials are doing! So, I would request you to spend some time going through AV Casino I & AV Casino II and provide us with your valuable feedback through the comments below. I would be looking out to hear from you.

 

Finally, thank you Mr. Rabbit!

I would like to thank our intern Prasanna for giving shape to this idea. Prasanna is one of the most curious mind I have seen, when it comes to understanding of human psychology and logic behind it. He is an avid reader and an impeccable fielder on a cricket field. He can be found in deep thoughts on his seat in AV House any day!
You are free to question him about his pseudonym B.Rabbit 🙂

addition rulebasics of probabilitymultiplication ruleprobability
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

Chandan Kumar
Chandan Kumar

I greatly appreciate your constructive move in circulating the knowledge on probability, I am glad to take the maximum benefit of this. Thanks again, great going!!

123

Ramdas
Ramdas

Nice article, very good concept to understand the concepts behind probability.

123

Kenneth
Kenneth

B Rabbit from 8 miles! :)

123

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