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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

Training recommendation (Tutorials from PyCon 2014 – USA) and contest update

Kunal Jain
Kunal Jain Last Updated : 17 Apr, 2015
3 min read

I am usually very selective about attending conferences!

Its not because I don’t like networking or talking to people. Its because I have a very high bar on time used in networking. I feel self-learning and discussing actual problems (when working on them) with people results in far more value for the time spent. However, there is one conference I want to attend at some point in future – PyCon (USA chapter).

Pycon 2014

For those who don’t know, PyCon is the largest annual gathering for the community using and developing the open-source Python programming language. While the conference is aimed to entire Python community (and not just data scientists), there is still a lot of learning and networking opportunities for data scientists. In India, the event is still not as big, but its recognition in industry is only a matter of time! This year, it is scheduled to be in September.

Pycon_india_2014

PyCon 2014 – USA happened recently (9th – 17th April) and I am sure it would have been a great platform for like minded people to interact and learn. As part of this conference, leading industry experts took a few tutorials. These tutorials are awesome source of knowledge, if you want to become awesome in using Python!

Here are my recommendations from these tutorials and the reasons why I like them:

  1. Exploring Machine learning with Scikit-learn and Diving deeper into Machine Learning with Scikit-learn – Just the right place to start learning about machine learning in Python. The 2 tutorials combined provide you with all the knowledge you need on the subject. The second tutorial covers some of the advanced topics like automated parameter tuning and how to scale up using iPython.parallel.
  2. Mining Social Web APIs with IPython Notebook – Coming from the author of popular book, mining the Social Web (O’Reilly, 2013), the tutorial and the accompanying iPython notebook provide you ways to extract data using social media APIs. Aimed towards beginners, the tutorial shows step by step extraction and mining of data. Once you know how to extract this data, you can get as creative as you want – How different are you from likes of your Facebook Friends? How much influence do you carry on Twitter? are just some of the questions to start your journey.
  3. Data Wrangling for Kaggle Data Science Competitions — An etude – A must read if you have participated in Kaggle competitions or want to at some point in near future. Krishna’s insights into how the competitions work and what data providers might do before providing data can save you months of scrolling through Kaggle Forums!
  4. Hands-on with Pydata: how to build a minimal recommendation engine – If you have not built a recommendation engine till now and are fascinated by the idea of building one, this is just what you needed. A tutorial which covers all the basics and quickly moves towards practical applications and the idea of iterational improvement.
I am left with another one Bayesian statistics made simple to watch – I am hoping that it is a good one as well. All these tutorials are awesome resources for learning Python for data analysis from some of the best minds across the globe.
Do you have any other tutorials which you would want to add to the recommendations? Please feel free to do so through comments below.

[stextbox id=”section”]A quick update on our blogoversary contest:[/stextbox]

We ran a 3 day contest asking 15 questions across our social media platforms. We saw awesome participation with really tough competition during these three days and are pleased to announce Datta Dharanikota as our winner. He gets a 3000 Rs. voucher from Amazon.

A few special mentions for their superb participation:  Nitesh Singh, Dhanman Gupta, Nimit Gupta, Raghavendra Reddy, Avinash Asuri, Jenny Srinivasan, Sateesh Kumar, Aditi Vij – well done guys and thanks for your participation! I hope that the contest gave you a small glimpse of the work we have done in last year! 

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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

Vaishali
Vaishali

Thanks for your post. PyCon is a conference for the Python community, organized by members of the Python community. PyCon is for Python enthusiasts of all experience levels, from new users to core developers. The original PyCon was formed in North America in 2003, but there are now a number of other conferences being run in the PyCon spirit around the world. Visit the worldwide PyCon web site for more information. PyCon gives you opportunities to: - See a variety of presentations, panels, and impromptu discussions. - Learn about significant advances in the Python development community. - Meet fellow developers from around the world. - Enroll in tutorials delivered by experts. - Participate in development sprints with fellow enthusiasts. The organizers aim to make the conference affordable and accessible to all.

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Anurag Reddy G V
Anurag Reddy G V

I am not able to find the I python code for the sessions mentioned above. Let me know if u have the link.

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