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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

Unveiling Analytics Vidhya Apprentice – a programme to graduate with recognition for your knowledge!

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

It has been more than a year since we started our journey to change how Analytics knowledge flows in communities. The experience has been rewarding, fulfilling, gratifying and filled with a lot of learning at the same time. In this short span, we have become one of the leading analytics blogs (in India) and have started transforming Analytics Vidhya into a knowledge portal.

Today, we unveil, a very special programme, Analytics Vidhya Apprenticeship (AV Apprenticeship) or the Data Science Apprenticeship, to take a leap forward in this direction.

The genesis of this programme lies in the numerous interactions, we have had with people across the globe in last one year. What stands out in these discussions, is the talent and recognition imbalance in the industry. Whether it is an experienced analyst (read lone warrior), trying to influence his customers or management about the benefits of investing in cutting edge infrastructure, or some one who has just graduated from one of the various certifications across the globe, recognition of your knowledge does not come easy.

Through this programme, we are set to change this forever (at least for a few people).

stage11

The proposition:

The Analytics Vidhya team will extend it’s learnings to a small batch of professionals, and mentor them to achieve great heights in their analytics career. For 6 months, these people will work along with the team, and gain practical knowledge about the subject. Some potential areas of work are:

  • Blogging – We train you on what it takes to become a successful storyteller / writer. Your high quality output gets published on our platform under your name and stays there even after the programmel (However, the content will come under the Analytics Vidhya Copyright)
  • Interactions / advice people on social platforms – You solve peoples’ queries and give them specific advice on various forums – be it Quora / Stackoverflow / Github / Kaggle
  • Active participation on Analytics Vidhya forums – It could be from starting discussions to answering people on specific subject matter.
  • Participate in data science competitions – You participate in various data science competitions and get mentored by the Analytics Vidhya team
  • Interact with people in industry – Conduct interviews with Industry leaders
  • Consultancy projects – Work on actual live problems to gain knowledge
  • Conducting guest lectures on behalf of Analytics Vidhya
  • Solve case studies

 

What do you gain from this programme?

Loads…starting from interactions with several like minded people, to interviewing experts from the Industry, to solving real life problems – you learn from every exercise. Your knowledge is showcased to all the readers on our platform, and hence you see the recognition coming accordingly.

The idea is, to create a unique programme, which focuses on implementing & showcasing your knowledge practically.

What do I need to pay for this awesome opportunity?

Well…nothing financially. You need to put in a lot of effort to go through this rigorous program. We will get the content and any financial rewards that come in from the work we do together.

However, in order to make sure that we only get serious candidates for the programme, we have introduced a very nominal deposit, which is INR 6000/- (~$100). The entire amount would be refunded to you, the day you pass out of the programme. In case you don’t finish the programme, this deposit would be treated as a compensation for our efforts (and hence will not be refunded).

You will need to clear a tough, multi-stage selection process to enter the programme. Following that, there will be a 6 month long rigorous apprenticeship.

P.S. – You only get the certificate post completion of programme. There are no marks for participating.

race_start

 

Selection process:

Stage 1: Complete the application on Analytics Vidhya website by 25th June 2014. This application includes several essays about your experience and the your reasons to join the programme. Next stages of selection would start as soon as you fill in the application and seats will be provided on first come, first serve basis. So don’t wait – go and fill in the application.

Your application will be selected / rejected basis your knowledge, passion and your essays. So, please fill them accordingly.

You will get more information about upcoming rounds after you register for stage 1.

P.S. At this stage, you don’t need to make any payment. We will collect this at a later stage.

 

Few other details:

  • This is not a traditional programme, but a practical hands on programme in Analytics. Do not expect class room sessions. Do expect a lot of analytics related discussions.
  • The onus to learn lies with you. If you need to learn any technical stuff, your mentor will direct you to the right place, but it is you, who needs to learn
  • Expect a lot of feedback and guidance on your work, be it on a blog post or a data science project.
  • It will be expected of you, to spend at least 4 hours every week as part of the programme.

We are very excited to launch this programme, and can’t wait to interact with you participants. Do let us know your thoughts / feedback / suggestions on this programme. If you need more details, feel free to write to me about it.

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

Nitesh
Nitesh

Hi Kunal , Thanks for launching such a wonderful programme but i have some queries . I don't have much experience in analytics industry but decided to shift my career to analytics so I have learnt SAS and R .Is this programme meant for such candidate . After getting selected someone will have work with you or can work from anywhere ,what will be the normal daily work for next six months. Thanks, Nitesh

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Kunal Jain
Kunal Jain

Nitesh, Thanks for raising the questions. The programme will have candidates from diverse background - most will have experience in analytics and a few would be newbies with certification like you. Candidate with least experience is set to benefit the most from experience of other people. The candidates can work from anywhere and will work on knowedge / content creation. They will be expected to spend 3 hours at their end and approximately 1 hour of online interaction with other people / Analytics Vidhya team. Hope this answers your question. Regards, Kunal

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Kiran Kumar
Kiran Kumar

Dear Mr.Kunal, I have been actively following your posts. I am an Masters student in Information systems from University of Erlangen , Germany. My deep interest interest towards Analytics made me write this message. Firstly, I thank you sincerely for launching such a wonderful course. I would like to take part in it. I participated in Data Analytics course on edx before. I hope that knowledge is sufficient. Moreover, I have deep interest towards Analytics. So, I am thinking that these are enough to sign up into the course.

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Kunal Jain
Kunal Jain

Kiran, Thanks for your message. First of all, this is not a course, it is an apprenticeship programme. We will take people from diverse backgrounds, which would include experts as well as novices. We will judge the applications on various parameters before making a decision and your chances of selection depend on relative strength within peer group. Hence, I would urge to apply at this stage and take the process forward. Thanks, Kunal

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Sudheer
Sudheer

Hello, I think your modus operandi is absolutely great. Keeping a nominal fee as 'security deposit' will ensure floating-population is kept at bay. I am strongly in favor of this initiative. I will be submitting my application too. I know the success of the pro-bono initiative depends significantly two things. 1. how serious the applicants are about learning and enhancing Analytical skills 2. if and how much time can working professionals (like me) can dedicate (or call it invest) in learning. I wish you guys good luck. Hope to be on the team.

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Kunal Jain
Kunal Jain

Hi Sudheer, Very well said. The success of the programme will depend on those factors. In order to make it more meaningful, we will take the candidates with varied background and expertise (e.g. someone who knows R, someone who knows SAS, someone with Matlab experience etc.) Looking forward to interacting with you in coming rounds. Regards, Kunal

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