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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

Update on new year resolutions (with training & reading recommendations)

Kunal Jain
Kunal Jain Last Updated : 28 Apr, 2015
4 min read

One of my mentors used to say:

“Any project / initiative that does not gets tracked, does not happen.”

While this might sound like a strong opinion, it turns out to be true in most cases. So, new year resolutions without a tracking mechanism in place will tend to end up as is!

I plan to regularly update you all on how I am doing on my resolutions for 2014. Hopefully, this will keep me motivated in achieving them and you get my learnings on the way from these posts.

For those who might have missed or have subscribed recently, I took following 4 resolutions at start of this year:

  1. More frequent usage of R and Python in my analytical endeavors
  2. Continue contributing actively in creating a community of analysts (in India)
  3. Re-start reading
  4. 5 kg weight loss

resolutions_2014

You can read more about these resolutions and how do I plan to track them here.

Coming to the important question, how has been the progress on these resolutions? Did I forget them after 1st week of January? 20th of January? or am I still on track to achieve all of these resolutions by end of year?

Well, the true answer lies somewhere in between. While I have not forgot the resolutions, I am still catching up with some of them. Here are the details:

[stextbox id=”section”]1. More frequent usage of R and Python in my analytical endeavors[/stextbox]

Between the two languages, I chose to focus on Python in first half of the year mainly because of 2 reasons:

  1. Higher degree of acquaintance between the two languages
  2. I want to compete on a few competitions on Kaggle this year, which has bigger Python community

So, I now have fully functional Python set up on my laptop. I have used it for doing small pieces of analysis. Additionally, I have registered for a Kaggle competition to construct an optimal portfolio of loans, which I plan to submit on Python.

 

Following are a few training and book recommendations which I used in last month:

[stextbox id = “section”]2. Continue contributing actively in creating a community of analysts (in India)[/stextbox]

January turned out to be best month till date for Analytics Vidhya, both in terms of inputs and outputs. We posted highest number of posts in a Calendar month (9) and saw a 35% growth in traffic over last month. The traffic has grown by 100% in last 4 months.

 

In addition to this, I have joined a couple of meetup groups in Delhi:

  1. Big data meetup
  2. AWS meetup group

 I am hoping to meet a few like-minded people in the coming events.

 

[stextbox id = “section”]3. Re-start reading[/stextbox]

I have managed to take time out for reading. Actually, I have been reading more than 2 hours a week! It feels great to get back to a past habit and it makes my week lot more fulfilling. Here are the books I read this month and my take on them:

  • Big data – A revolution that will transform How we live, work and think by Viktor Mayer & Kenneth Cukier – A book laying out basics about big data. Good if you are completely new to the topic. Don’t expect any technical details in this book. Good, if this is your first book on the matter.
  • The Lean startup by Eric Ries – Currently in middle of reading this. Loved the book till now. The principles laid out in the book apply to any business / start-up from day 0.
  • The everything store: Jeff Bezos and the age of AMAZON by Brad Stone. I respect Jeff Bezos as a leader and admire what he has created Amazon to be. This is the first book which tells so much detail about him and his management style.

 

 [stextbox id = “section”] 4. Weight loss – 5 kg [/stextbox]

This is the resolution which I have failed to keep up with. Although I have become more active during the day, I have not been able to put an exercise schedule. In addition to this, the sleep has been irregular and there have been days when I binged on junk food in middle of night!

I am hoping that I will catch up on this resolution in coming couple of months and be in good shape by end of year. 

 

How has been your progress on your resolutions? Are you on track for keeping them? Or they are lying somewhere at the back of your mind? What will you bet upon – I will complete my resolutions or I’ll leave them mid-way? Stay tuned for next update (March end)

Also, have you undergone any training / tutorial in last one month or have any reading recommendation to share? If so, please do so with the group.

If you like what you just read & want to continue your analytics learning, subscribe to our emails 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

Shaifali
Shaifali

Hey Kunal My resolution for this year is to complete 2 Data Analytic projects; have started working on one and it is the same Kaggle Optimal Portfolio of loans( felt intrigued while reading it above :)). I have joined it about a week ago. ATM I have no idea what I am gonna to do in it further, first time saw such big dataset with 800 features, even I am unable to open whole dataset on my desktop; managed to look it by splitting it into small parts; afterwords I will virtual environment that allow me to work on large data remotely. I am in its first phase, took first 100 rows of Train dataset, analysing it using Python and then after completely concluding results from this small part I will move to work on whole data. I will learn how to deal with Curse of Dimensionality, have only heard about curse don't know its solution. So looking forward to gather more and more knowledge.

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Kunal Jain
Kunal Jain

Hi Shaifali, Delight to hear that you are progressing well on your resolutions. You are right about size of dataset. You might be able to open it on a 64 bit architecture and higher RAM. Keep me posted on how you are progressing on the competition. Regards, Kunal

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