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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning LifecycleIntroduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data InsightsDescriptive StatisticsInferential StatisticsHow to Understand Population Distributions?
Reading Data Files into PythonDifferent Variable Datatypes
Probability for Data ScienceBasic Concepts of ProbabilityAxioms of ProbabilityConditional Probability
Central Tendencies for Continuous VariablesSpread of DataKDE plots for Continuous VariableOverview of Distribution for Continuous variablesNormal DistributionSkewed DistributionSkeweness and KurtosisDistribution for Continuous Variable
Central Tendencies for Categorical VariablesUnderstanding Discrete DistributionsPerforming EDA on Categorical Variables
Dealing with Missing ValuesUnderstanding OutliersIdentifying Outliers in DataOutlier Detection in PythonOutliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and PopulationCentral Limit TheoremConfidence Interval and Margin of Error
Bivariate Analysis Introduction
CovariancePearson CorrelationSpearman's Correlation & Kendall's TauCorrelation versus CausationTabular and Graphical methods for Bivariate AnalysisPerforming Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical VariablesIntroduction to Hypothesis TestingP-valueTwo sample Z-testT-testT-test vs Z-testPerforming Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares TestBivariate Analysis on Categorical Categorical Variables
Multivariate AnalysisA Comprehensive Guide to Data ExplorationThe Data Science behind IPL
Supervised Learning vs Unsupervised LearningReinforcement LearningGenerative and Descriminative ModelsParametric and Non Parametric model
Machine Learning PipelinePreparing DatasetBuild a Benchmark Model: RegressionBuild a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should knowConfusion MatrixAccuracyPrecision and RecallAUC-ROCLog LossR2 and Adjusted R2
Dealing with Missing ValuesReplacing Missing ValuesImputing Missing Values in DataWorking with Categorical VariablesWorking with OutliersPreprocessing Data for Model Building
Understanding Cost FunctionUnderstanding Gradient DescentMath Behind Gradient DescentAssumptions of Linear RegressionImplement Linear Regression from ScratchTrain Linear Regression in PythonImplementing Linear Regression in RDiagnosing Residual Plots in Linear Regression ModelsGeneralized Linear ModelsIntroduction to Logistic RegressionOdds RatioImplementing Logistic Regression from ScratchIntroduction to Scikit-learn in PythonTrain Logistic Regression in pythonMulticlass using Logistic RegressionHow to use Multinomial and Ordinal Logistic Regression in R ?Challenges with Linear RegressionIntroduction to RegularisationImplementing RegularisationRidge RegressionLasso Regression
Introduction to K Nearest NeighboursDetermining the Right Value of K in KNNImplement KNN from ScratchImplement KNN in Python
Bias Variance TradeoffIntroduction to Overfitting and UnderfittingVisualizing Overfitting and UnderfittingSelecting the Right ModelWhat is Validation?Hold-Out ValidationUnderstanding K Fold Cross Validation
Introduction to Feature SelectionFeature Selection AlgorithmsMissing Value RatioLow Variance FilterHigh Correlation FilterBackward Feature EliminationForward Feature SelectionImplement Feature Selection in PythonImplement Feature Selection in R
Introduction to Decision TreePurity in Decision TreeTerminologies Related to Decision TreeHow to Select Best Split Point in Decision Tree?Chi-SquaresInformation GainReduction in VarianceOptimizing Performance of Decision TreeTrain Decision Tree using Scikit LearnPruning of Decision Trees
Introduction to Feature EngineeringFeature TransformationFeature ScalingFeature EngineeringFrequency EncodingAutomated Feature Engineering: Feature Tools
Introduction to Naive BayesConditional Probability and Bayes TheoremIntroduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings!Working of Naive BayesMath behind Naive BayesTypes of Naive BayesImplementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification ProblemEvaluation Metrics: Multi Class Classification
Introduction to Ensemble TechniquesBasic Ensemble TechniquesImplementing Basic Ensemble TechniquesFinding Optimal Weights of Ensemble Learner using Neural NetworkWhy Ensemble Models Work well?
Introduction to StackingImplementing StackingVariants of StackingImplementing Variants of StackingIntroduction to BlendingBootstrap SamplingIntroduction to Random SamplingHyper-parameters of Random ForestImplementing Random ForestOut-of-Bag (OOB) Score in the Random ForestIPL Team Win Prediction Project Using Machine LearningIntroduction to BoostingGradient Boosting AlgorithmMath behind GBMImplementing GBM in pythonRegularized Greedy ForestsExtreme Gradient BoostingImplementing XGBM in pythonTuning Hyperparameters of XGBoost in PythonImplement XGBM in R/H2OAdaptive BoostingImplementing Adaptive BoosingLightGBMImplementing LightGBM in PythonCatboostImplementing Catboost in Python
Different Hyperparameter Tuning methodsImplementing Different Hyperparameter Tuning methodsGridsearchCVRandomizedsearchCVBayesian Optimization for Hyperparameter TuningHyperopt
Understanding SVM AlgorithmSVM Kernels In-depth Intuition and Practical ImplementationSVM Kernel TricksKernels and Hyperparameters in SVMImplementing SVM from Scratch in Python and R
Introduction to Principal Component AnalysisSteps to Perform Principal Compound AnalysisComputation of Covariance MatrixFinding Eigenvectors and EigenvaluesImplementing PCA in pythonVisualizing PCAA Brief Introduction to Linear Discriminant AnalysisIntroduction to Factor Analysis
Introduction to ClusteringApplications of ClusteringEvaluation Metrics for ClusteringUnderstanding K-MeansImplementation of K-Means in PythonImplementation of K-Means in RChoosing Right Value for KProfiling Market Segments using K-Means ClusteringHierarchical ClusteringImplementation of Hierarchial ClusteringDBSCANDefining Similarity between clustersBuild 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 DaskWorking with CuML
Introduction to Machine Learning InterpretabilityFramework and Interpretable Modelsmodel Agnostic Methods for InterpretabilityImplementing Interpretable ModelUnderstanding SHAPOut-of-Core MLIntroduction to Interpretable Machine Learning ModelsModel Agnostic Methods for InterpretabilityGame Theory & Shapley Values
Introduction to AutoMLImplementation of MLBoxIntroduction to PyCaretTPOTAuto-SklearnEvalML
Pickle and JoblibIntroduction to Model Deployment
Deploying Machine Learning Model using StreamlitDeploying ML Models in DockerDeploy Using StreamlitDeploy on HerokuDeploy Using NetlifyIntroduction to Amazon SagemakerSetting up Amazon SageMakerUsing SageMaker Endpoint to Generate InferenceDeploy on Microsoft Azure CloudIntroduction to Flask for ModelDeploying ML model using Flask
Model Deployment in AndroidModel 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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Data science podcasts – suitedclerk
Data science podcasts – suitedclerk

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

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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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