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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

The DataHour: How to Transition into Data Science?

Anshul Singh
Anshul Singh Last Updated : 03 Jun, 2022
3 min read

Dear Readers,

I appreciate you coming onto our platform and expanding your knowledge. I am sure, by now, some of you must be interested to make a transition into the Data Science industry as it’s one of the most host-selling jobs (if we can put it that way :D). So, this DataHour session is dedicated to all our readers who have been thinking to take a leap of faith and make this transition happen!

Sutirtha Chakraborty is currently working as Head at Global Data Science & Analytics, Abbott. And, he is ignited to share his knowledge and personal experience in the world of ‘Data Science’ with you all. I know most of you will have certain apprehensions about it and it’s all very understandable. So, leverage this opportunity and ask all your doubts pertaining to the transition that you have been thinking through!

Transition into Data Science | The DataHour

BOOK YOUR SEAT NOW! 🛋️

About the DataHour

Data Science is the new umbrella term for all ‘Data Analysis’ related work in the Analytics space across all industries. Its exponentially growing popularity over the last few years has been primarily fueled by the massive growth in the amounts of data generated regularly in these industries along with an unprecedented improvement in the computational tech infrastructure and innovation of various transformational methods that have solved some of the most complex real-world problems at scale.

This has brought along a vast number of interesting and lucrative job opportunities across all these industries, striking a deep interest in people with a wide variety of academic backgrounds to switch to a Data Science job. But this transition is not easy at all – in fact, it is very hard, especially for those people who don’t come from a traditional Statistics or Computer Science background. 

In this DataHour, discover how you can successfully transition into a Data Science job and build a solid career profile in it.

Prerequisites: A genuine interest in Data Science

Who is this DataHour for?

  • Students & Freshers who want to build a career in Data Science
  • Working professionals who want to transition to a Data Science career

Speaker

The DataHour

Sutirtha Chakraborty

Head – Global Data Science & Analytics, Abbott

Sutirtha has 20+ years of experience in Statistics and Machine Learning, Healthcare Research & Development and Product Management. Prior to joining Abbott, Sutirtha was associated with Neference Labs Private Limited as a Senior Director of Engineering (AI/ML). In the past, Sutirtha has also worked with GlaxoSmithKline and Novartis as a Data Science Leader.

Sutirtha served as a Postdoc in Statistical Genomics at the Harvard University and Dana-Farber Cancer Institute. He has a PhD in Biostatistics from the University of Louisville, KY, USA, an MSTAT in Applied Statistics and Data Analysis from the Indian Statistical Institute, Kolkata and a BSc in Statistics (Hons) from the Presidency University, Kolkata.

Conclusion

Grab this fantastic opportunity by registering here for the DataHour Webinar. If you’re attending this session and have some preliminary questions about this topic, please send them to us at [email protected], or you could ask directly to the speaker during the session.

If you missed our previously conducted ‘The DataHour’ series, head to our YouTube Channel and check out the recordings. Read the synopsis about the previous held DataHour sessions on our blog here.

Connect

If you’re facing any difficulty registering or wish to conduct a session with us. Then, get in touch with us at [email protected].

BOOK YOUR SEAT NOW! 🛋️

career transitiondata scienceData Science TransitionThe DataHourwebinar
Anshul Singh
Anshul Singh

Securing the fourth spot in the list of top 10 blogs on machine learning published on AV in 2022 is a blog about ‘Heart Disease Prediction Using Machine Learning’ coined by author Aman Preet Gulati. The blog is technically-dense and covers everything to know about heart disease prediction. Created by one of our popular guest authors, Aman Preet Gulati, this blog is a must-read for all inclined toward harnessing ML’s power in healthcare to reap powerful outcomes.

In this article, we will be closely working with the heart disease prediction and will look into the heart disease dataset. From that dataset, we will derive various insights that help us know the weightage of each feature and how they are interrelated to each other, but our sole aim this time is to detect the probability of a person being affected by a saviour’s heart problem or not.

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Responses From Readers

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Diksha
Diksha

Great to see your website content on Data Science. It was very well informative & knowledgeable content. I have also explain blog related to Data analysis which is a part of Data Science

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