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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

Welcome 2017 – Are you prepared for a year of data based disruption?

Kunal Jain
Kunal Jain Last Updated : 03 Jan, 2017
3 min read

We are in exciting and challenging times. The pace of change in data science industry is increasing by the day. It is difficult to understand the kind of impact data science would make to our life. I happened to be in my home town on the new years eve and caught up with some old buddies. One of them was a Chartered Accountant (CA), another one had his own clothing business and one of them was in professional education.

Surprisingly, none of them realized the impact data science and automation would have on their work. The CA believed that accounting requires too many human interventions to be automated even in next 5 – 10 years, the friend running clothing business had not heard about Internet of Things and the one in professional education said that professional education can not be moved to Open Courses as they don’t make people industry ready. None of them was thinking data either as an opportunity or a threat.

You could imagine how the next few hours would have gone with me being in the room!

 

The changing landscape

Take a step back and think what all happened in 2016. AlphaGo defeating Lee Sedol, Tesla adding self driving capabilities to its cars, chat bots revolutionising the way we interact with our customers, TensorFlow grew in popularity, early AI assistants in form of Google Assistant, Alexa, Cortana & Siri – these are just a few examples which come to top of the mind.

I am sure 2017 would be even more eventful. Data generation and number of connected devices would only increase. We would see better IoT platforms and their mass usage later in the year. We will see increasing number of AI bots coming out which can act as meaningful assistants. We are still some time away from a generic agent, but there are enough specific applications for an AI assistant. Sales of Amazon Alexa Dot during Christmas goes on to show some early signs of success.

The bottom line is irrespective of whether you like it or not, data science, machine learning & artificial intelligence will change your life and 2017 would be the first year when a layman start realizing the depth of this impact.

 

2017 for Analytics Vidhya

This changing landscape means a huge opportunity for Analytics Vidhya. Not only we will see more and more professionals entering in the industry, but we will also see the increased pace of up-skilling. Which means that we will continue to help our community members learn about the new tools and techniques. You will see more guides and tutorials coming on new areas like IoT, Artificial Intelligence and other data related domains.

But this year, we will take our engagement with you miles ahead. We will challenge you to participate in various competitions, skill tests and hackathons. And we will continue to find you the best jobs for you in the industry. We will provide you more opportunities to interact with the industry experts – our meetups, webinars and learnups will continue to do this for you.

This year, we will not measure our success on our penetration or the traffic on the website. We will measure our success by the number of users we engaged with and made a difference in their career.

 

Kickstart January 2017

We are all set to define the momentum for 2017 this month. We plan to bring you awesome guides, hackathons, jobs, webinars and meetups for you in January. You can check out the contests coming up here.

We will start the year with Skilltest on SQL and a Offline workshop in Chennai. SQL is a must have skill for every data professional and the event in Chennai is meant to provide mentorship and guidance to beginners in Chennai.

Later in the month, we have Strategic Ball and Deep Learning skill test. If you are a student from India, do check out Data Tales competition from Great Lakes Institute of Management.

 

What are you doing in 2017?

I am sure we will get you started with your learning and agenda. But, we would want to know, what are your plans for 2017? What areas do you plan to learn? Which tools and techniques will you plan to learn? What kind of competitions are you looking out for?

Do let us know through the comments below and we will make sure that we help you out in every possible manner through out the year.

Hope you have a rocking year 2017!

 

Learn, compete, hack and get hired!

data disruptionhackathonsskilltests
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

yash
yash

Hi Kunal, I have registered for PGPBA and my batch starts this month. So my resolution/plan for 2017 is clear. I want to gain maximum knowledge that I can in Data Science. and from AV I request to have few articles on domain perspective. Most of the articles and learning that we see here are related to a specific technique or a tool. As stated by you itself in few articles, domain knowledge is also very critical in taking decisions and building models. So my request to AV is on domain related articles.

123

Pradeep
Pradeep

HI, Thank you. This year would be focusing more on Deep learning and the projects related to that.

123

Pradeep
Pradeep

The competition related to artificial intelligence, image processing and deep learning would be more useful.

123

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