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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

Notes, impressions, experience and excitement from PyCon India 2015, Bengaluru

Kunal Jain
Kunal Jain Last Updated : 11 Dec, 2015
5 min read

If there is one conference I love to follow, it is PyCon! We have been following the PyCon U.S. for last 2 years and I have learnt a lot by watching the videos of the talks and the workshops happening in PyCon. This time I decided to attend the India chapter of PyCon – PyCon India 2015, which happened in Bengaluru from 2nd – 4th October 2015.

Due to some earlier commitments, I could not attend the workshops on 2nd. But, I made sure to make the most of the event in remaining 2 days. Following were some notes and impressions I carried from PyCon India:

pycon india 2015

 

Awesome turnout at the event

It was heartening to see the turn out at the event. We got to know later that more than 1500 people registered for the event with ~20% females. This was probably the largest tech event I have personally attended in last 10 years. For the entire 2 days, the event received jam packed auditoriums with people full of enthusiasm and energy. Here is a snippet of 4th morning, I gathered from Twitter:

PyCon1

 

Profile of the attendees

My initial impression was that the conference had higher presence of students / developers in early stages of career than I had thought. However, I think this was more to do with the fact that it was difficult to reach to venue on the morning of event, if you are coming from outside Bengaluru (the airport was about 2 hours drive from the venue). As the conference proceeded, I could see more senior developers, data scientists and pythonsits making their way to the event.

 

Keynotes & KG award

I loved the fact that key notes on both the days and KG award were given to people leading and actively involved in Python community. Here are the details about the speakers and their brief profiles:

  • The keynote speaker for Day 1 was Dr. Ajith Kumar, who is a M.S. & Ph.D. in  in Nuclear Physics from University of Calicut and M.Sc in Accelerator Physics, from University of Manitoba. For last few years, he has been involved in promoting Free Software tools in Education and leading a project for developing open hardware laboratory equipment for teaching science, that uses Python for data analysis and visualization.
  • Keynote speaker for Day 2 was Nicholas H. Tollarway a software engineer, classically trained musician, philosophy graduate, teacher and writer! His passion for education stood out and so did his efforts on community building through his work on running Python Dojo. Here is the link to his presentation. I hope that the video of his talk is uploaded on YouTube soon for the benefit of larger audience. He demonstrated use of a small board, he plans to use to teach coding to children which will be open sourced next year. Inspiration, inspiration, inspiration! #PyconIndia starts trending on Twitter on 4th morning.
  • The recipient of KG award was Mr. Vijay Kumar. He was again an inspiration for his simplicity and dedication to community building in Chennai. ChennaiPy gets 80 – 100 people attending their meetups and their arrangements and support for the event stands out.

 

The nominated talks – Let Python-someness begin

Coming to the main part of the event, the talks were nominated and voted by the community. You can see the complete list of talks here. Each of the talk had high quality content and participation from the audience. Here are some of the talks associated with Data Science, which you can watch whenever the videos are up:

One of the thing which stood out during the event was that the entire event was run on time.

 

The lightening talks and Shakthimaan

Lightening talks are essentially small 3 minute talks by people to share their work, passion or any thing else (of course related to Python) with the audience. They were crisp talks, some focusing on fun, while other on their hacks and information.

A special mention to the lightening talk from Shakthimaan – he delivered nursery rhymes & yet another lightening talk flawlessly. While I can’t replicate the experience and his skill in delivering the talk, you can still go ahead and read the content.

Since, there was a lot of enthusiasm in the room for data science, we also made a lightening talk about what we do at Analytics Vidhya.

 

Open Space on Data Science:

Open space was another awesome format, where people could gather outside the talks and discuss about any topic they want with others. These were floated on a board and people could join the open talks on their own. We found one of Data Science and met some of our followers and data hackers here. Here are some stills from the open space on data science:

open_space_data

It was pleasure to meet Bargava & Dorai in person among other followers. Bargava also prepared a poster on Ensemble modeling, which was put on display.

 

My Notes and Takeaways:

  • It was amazing and quite refreshing to meet other people involved in community building. Some of them leave an inspiration while others lead a helping hand. At then end of the day, it feels like meeting a few alter ego, who enjoy the journey of creating a community more than any output attached to it.
  • PyCon India is lucky to have such enthusiastic and relentless volunteers, who made sure the event was a success. The entire event is run by volunteers and the outcome from this group was flawless!

PyCon_volunteers

  • There was a lot of enthusiasm for data science and machine learning in the room (oops! the hall!). I think all the talks on these topics probably received the most number of questions and engagement from the audience – probably a sign of things to come.
  • I was pleasantly surprised by the show of hands from people who knew about Analytics Vidhya during my lightening talk – thanks to all our readers and audience for following us and spreading the word. We will make sure that this awareness increases multi-folds in the years to come!

 

End Notes:

Like all events, PyCon India came to a close. I walked out refreshed from the event carrying a big smile and lot of warmth from our followers. The event left me inspired and motivated for putting in more efforts in time to come! See you every one again at PyCon India 2016!

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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

Shakthi Kannan
Shakthi Kannan

Hi Kunal, Thanks for the wonderful write-up and feedback. Can you please change the spelling to use "Shakthimaan" in the post? I use two H's in the name. Best wishes, SK

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Kunal Jain
Kunal Jain

Done! Thanks for pointing it out - my bad!

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