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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

10 Data Science, Machine Learning and AI Podcasts You Must Listen To

Pranav Dar
Pranav Dar Last Updated : 26 Jul, 2020
5 min read

With the rapid pace at which technology is driving innovation in machine learning and artificial intelligence, it has become immensely important to keep pace with the ongoing trends in data science. However, it can become challenging to read everything that’s out there.

Podcasts are a great alternative to keep yourself updated. With the remarkable rise of the data science industry in recent years, enough podcasts have been created for us to geek out over.

In this article, we look at 10 such podcasts that we feel any data scientist must listen to:

 

Analytics Vidhya’s own DataHack Radio Podcast!

This is Analytics Vidhya’s exclusive podcast series which features top leaders and practitioners in the data science and machine learning industry. In fact, the first three episodes are already available! Listen and subscribe now!

It’s available on SoundCloud, iTunes, Google Podcasts and of course, our own site!

Average episode duration: 60 minutes

Total Number of Episodes: 23

Area(s) of focus: Data science, machine learning, deep learning, artificial intelligence

 

The Data Skeptic

With episodes ranging from anywhere between 15 minutes to an hour, the Data Skeptic is a great way to introduce yourself to the world of Data Science podcasts. The topics include interviews with data science practitioners to talk about real world data science challenges, simple academic concepts like feature selection, NLP, decision trees, among may others.

If you have to narrow down to one podcast, make sure it’s this one.

Average episode duration: Anywhere between 15-60 minutes

Total Number of Episodes: 198

Area(s) of focus: Data science concepts and real-world issues

 

The O’Reilly Data Show

This can get technical and quite in-depth at times, but it’s still a great way of keeping up to date with what’s happening in the world of AI and Machine Learning. It’s hosted by O’Reilly Media’s Chief Data Scientist, Ben Lorica.

Average episode duration: 20-60 minutes

Total Number of Episodes: 60

Area(s) of focus: Technically driven, deals with current issues

 

Concerning AI

This podcast offers a slightly different take on AI – it looks at the threats and risks that the growing influence of AI can have on today’s society, and what steps we need to take to combat it.

Average episode duration: 20-40 minutes

Total Number of Episodes: 62

Area(s) of focus: Regulations and concerns in the AI world

 

Data Stories

Data Visualization is at the heart of this podcast. Hosts Enrico Bertini and Moritz Stefaner interview folks from various fields every week. Recent topics include Data Pottery, Bitcoin Visualizations and a fascinating episode of “What’s Going on with this Graph?”.

Average episode duration: 30-50 minutes

Total Number of Episodes: 112

Area(s) of focus: Data visualization

 

Learning Machines 101

Their aim is to “demystify the field of Artificial Intelligence by explaining fundamental concepts in an entertaining manner”. Their topics do tend to get technical sometimes, like “How to use Expectation Maximization to Learn Constraint Satisfaction Solutions” or “How to Use Radial Basis Function Perceptron Software for Supervised Learning”.  However, some topics are meant for all listeners regardless of technical knowledge.

Average episode duration: 20-30 minutes

Total Number of Episodes: 69

Area(s) of focus: Technically driven, intermediate to advanced machine learning concepts

 

Women in Data Science (WiDS) Podcast

I’m a big fan of the Women in Data Science (WiDS) series. In the WiDS podcast, Stanford Professor Margot Gerritsen interviews women across the data science profession as they share their career highlights, advice, and lessons learned along the way.

It’s quite an inspirational podcast and covers a wide range of domains, from healthcare to seismology to human rights and more.

Average episode duration: 30-40 minutes

Total Number of Episodes: 9

Area(s) of focus: Data science, machine learning, data science career

 

Artificial Intelligence in Industry (Dan Faggella)

Every week, Dan Faggella interviews data scientists and AI leaders from the world’s companies to figure out the applications and implications of AI. There are tons of episodes you can listen to from the last couple of years that are still relevant. The latest episode, “Will you buy your home or car using AI?” is a very pertinent topic in today’s society.

Average episode duration: 30 minutes

Total Number of Episodes: 99

Area(s) of focus: Interviews with data practitioners, discussions on current topics

 

Talking Machines

If you’re new to data science or are not a fan of the technical stuff, this podcast is for you. Each episode features interviews with industry experts about data science and gives a holistic overview of a few techniques. Most episodes also feature listeners calling in and asking questions.

Average episode duration: 60 minutes

Total Number of Episodes: 29

Area(s) of focus: Basic to intermediate data science concepts, listener Q&A , interviews with industry experts

 

This Week in Machine Learning & AI

Episodes in this podcast are churned out at a fairly regular interval every week. This features interviews with AI /ML experts on a variety of data science topics.

Average episode duration: 45 minutes

Total Number of Episodes: 111

Area(s) of focus: Current topics in ML and AI including projects and companies

 

Linear Digressions

The hosts, Ben Jaffe and Katie Malone, manage to break down complex data science problems and techniques into snippets of information that can be easily digested by the casual listener.

Average episode duration: 15 minutes

Total Number of Episodes: 164

Area(s) of focus: Data science and machine learning concepts applied to real-world issues

 

Freakonomics Radio

Disclaimer: This isn’t exclusively a data science podcast. Host Stephen J. Dubner explores a whole host of puzzling issues in the world but it gives the listener a good idea of how data science can be integrated into economics and other global issues.

Average episode duration: 45 minutes

Total Number of Episodes: 310

Area(s) of focus: Economics, interviews with social scientists

Are there any other podcasts you listen to? Add them in the comments section below!

Note: The total number of episodes mentioned in this article are updated till the publishing date.

Pranav Dar
Pranav Dar

Senior Editor at Analytics Vidhya.Data visualization practitioner who loves reading and delving deeper into the data science and machine learning arts. Always looking for new ways to improve processes using ML and AI.

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Responses From Readers

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Data science podcasts – suitedclerk
Data science podcasts – suitedclerk

[…] https://www.analyticsvidhya.com/blog/2018/01/10-data-science-machine-learning-ai-podcasts-must-liste… […]

123

Vishalkumar Arvind Singh
Vishalkumar Arvind Singh

How can i get this courses

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Pranav Dar
Pranav Dar

Hi Vishualkumar, These are podcasts you can listen to. You can access each podcast by clicking on the names mentioned in the above article.

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Ghouse
Ghouse

I need to know more about it and I want to be part of it

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