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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

My resolutions for 2014

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

In my last post, I mentioned how 2013 has been a phenomenal year for me. I can’t wait to continue the momentum in 2014. The year looks full of promises, a lot of learnings and value creation.

So what are my plans for 2014?

New_Year_wallpapers_Happy_new_2014_year

I plan to take a few resolutions and then track myself against them. I think that doing so guides and anchors my growth as a person. Last year, I completed 3 out of 4 resolutions I took. I am hoping to hit 100% achievement this year.

[stextbox id = “section”]Guidelines:[/stextbox]

While the web is full of suggestions on how to and how not to make resolutions, I will just mention the guidelines I follow to create my resolutions:

  • I usually keep only 3 – 4 resolutions in a year. Any more than that and there is a huge risk of not achieving majority of them (Yes, there have been years when I wanted to achieve more than 10 of them!).
  • The intention is to have measurable and objective resolution. So each resolution can be handed to a third person who can judge me on them at the end of year and tell me my success rate.

 

[stextbox id = “section”]Resolutions:[/stextbox]

So here are the resolutions I have kept for myself for 2014:

  • More frequent usage of R and Python in my analytical endeavors. I rely heavily on SAS and excel for the work I do. While I have learnt R & Python and used R sparingly, I am still some steps away from executing entire project on both these languages. By end of year, I plan to deliver at least 1 project each on R and Python independently.
  • Continue contributing actively in creating a community of analysts (in India): While the journey of Analytics Vidhya started last year, I plan to continue and increase the ways this platform becomes useful for development of a analyst community (specifically in India).

So, how would I measure this? The outputs I will measure are obviously readership and audience engagement on Analytics Vidhya. The inputs I will track myself against are number of posts, number of conferences attended / presented.

  • Re-start reading: I used to religiously read for ~30 minutes before hitting bed every day. However, this changed after we were blessed with Jenika (my daughter). My mid-night baby sitting and blog engagement have left little room for reading at night. I’ll re-start reading for an hour at least 2 days in a week.
  • 5 kg weight loss: This is the only un-ticked resolution from last year. I am hoping to reduce some belly fat I have accumulated over last few years. My family and friends will tell you that I write this every year and the strike rate has been low. I am hoping to change it this year.

So, here is my reminder sheet for rest of the year:

resolutions_2014

That’s it! I’ll keep you posted on how I track against these during the year and I am hoping to hit 100% achievement on each of these.

What are your resolutions for the year? How do you plan to grow as a professional? If you have set aside resolutions, please feel free to post them and we can track ourselves collectively. If not, would you want to join?

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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

Ram
Ram

Great resolutions!! Have started compiling information on R - http://r.ramganalytics.com/r/ You does not seem (from photo) to have any issue with weight.. :) Thanks Ram www.ramganalytics.com

123

Kunal Jain
Kunal Jain

Hi Ram, Thanks for the link on resources on R. The photo was before the weight gain ;) Regards, Kunal

123
aanmol
aanmol

Excellent Kunal''''''''''

123

Kumar Chinnakaliu
Kumar Chinnakaliu

Cheers New Year 2014 ! Here are my resolutions to go with, 1) Usage of Hadoop and R in Big Data Analytics project 2) Get Certified, in Industry Big Data Analytics 3) Engage with the Open source Community ( Big Data Analytics area) 4) Once in a week spend quality time with family and my angel "Anuja Kumar" "Analytics Vidhya" is awesome resource for both beginners and experienced. Hats Off Team. Regards, Kumar Chinnakali

123

Kunal Jain
Kunal Jain

Good resolutions Kumar Chinnakali. Lets track ourselves against how we are doing regularly. Where do you plan to get the certification from? Kunal

123

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