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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

7 Must Watch Documentaries on Statistics and Machine Learning

A
Analytics Vidhya Last Updated : 26 Jun, 2020
4 min read

Introduction

“Soon, our habitat will be invaded by unreal humans. Not only they’ll influence our way of living, but also intervene in our modus operandi.”

I’m not the only one who thinks this way. Last week I released a list of must watch movies on Machine Learning and Data Science. I’ve watched 8 of them till now. These movies reveal the smart use of data and machine learning to make our lives better.

Today, I am listing down some of the best documentaries on Data Science, Statistics and machine learning that I have come across.

A documentary is nothing but a nonfictional representation of an incident. It consist of factual arguments and real life events. Unlike a movie, documentaries are more realistic and connected to a real-life purpose.

 

Why should you watch these documentaries?

We live in a sphere of technological advancements which is majorly driven by data. A decade back, the use of data was limited for statistical measures and studies. But now, everything has changed. Data influences our lives at personal and professional level. The combination of data, programming and mathematics have completely transformed the ways of doing business. Even businesses like wall street trading, casino gambling, sports are largely driven by data mining methods.

That’s interesting ! But how did it all start ?

Documentaries below will try to answer this question. If you are also a crazy soul for data science and machine learning, you’ll find them worthy. These documentaries are based on Statistics, Big Data, Artificial Intelligence, Mathematics and related topics.

must watch videos on machine learning statistics

 

Must Watch Documentaries

The Joy of Statistics 

Duration: ~ 60 minutes

For those of you who find statistics boring, here is a proof that it’s ‘not’. It’s a breath-taking documentary which emphasizes on the use of statistics in real life. It will change the way you look at data and statistics. We fail to see the large influence of statistics around us. As Hans Rosling says, ‘We fail to reckon but statistics is giving us a great understanding of life and universe beyond.’ There discover a lot of joy in these 59 minutes.

https://www.youtube.com/watch?v=g9nvLqLM9Y0

 

Future Intelligence

Duration: ~ 43 minutes

Humans are a curious species of living beings. We find excitement in exploring things which can improve our lives.  We always strive to achieve better than yesterday. We are only limited by our imagination. If you are also one of the kind who relate to last few statements, this amazing documentary will surely stimulate your curiosity levels. You’d find devices which would revolutionize our lives in coming future. This is the power of technology and data.

https://www.youtube.com/watch?v=WGZHMX_Srk4

 

The Smartest Machine on Earth

Duration: ~52 minutes

In early 2011, IBM created a super computer named ‘Watson’ to challenge the human contestants on the popular game show Jeopardy. Here is the complete story of watson. You’d be surprise to know that watson had a brain size of 2400 home computers. This computer used techniques such as text analytics, machine learning algorithms to find the correct answers. Watson was then used for humans welfare and successfully benefited 34 million customers through WellPoint health plans.

https://www.youtube.com/watch?v=tAzeGkuQmUU

 

Humans Need not Apply

Duration: ~ 15 minutes

Will a robot replace your job? In the last decade, several tools have been built which are ready to replace human labour. As advanced version of these tools are ‘Robots’. Companies have heavily invested in establishing artificial intelligence systems to reduce the chances of human error.In order words, robots have started replacing low skilled human jobs. In this short documentary, you’ll learn about the changes ready to come in near future and some amazing stuffs which robots can do effortlessly.

 

Let my Data set Change Your Mindset

Duration: ~20 minutes

I did not include this TED talk in my list until I had watched it completely. Though, it doesn’t belong to the family of documentaries, yet I would insist you to watch it. In these 20 minutes, you’ll learn the importance of impeccable story telling using data visualization. A data scientist must master this skill. Mr. Rosling, beautifully demonstrates the use of statistics by comparing the life expectancy of various countries. For more interesting videos by Hans Rosling on statistics, visit here.

https://www.youtube.com/watch?v=jjPwyNI6eKo

 

Deluged by Data

Duration: ~43 minutes

I always wanted to tell people the importance of data in our lives. But, I always used to run out of words until I found this documentary. It made my job easier. In these 43 minutes, you’ll experience a huge influence of data in your daily life. The devices which you use, tracks enormous amount of data overtime and gives businesses to other companies. Should you worry about this ? You really to watch it to get the answer. Due to these innovative methods of data collection, we are experiencing a massive surge in demand of data scientist.

dm1

 

The Age of Big Data

Duration: ~ 60 minutes

Data revolution is set to become more personal. Data scientists look for patterns in data sets. These patterns are dynamic in nature. An insight derived today might become obsolete tomorrow. In these 60 minutes, you’ll learn about the ‘common’ sources of data which adds upto 2.5 billion GB of data generated every day. Moreover, you’ll learn about the impact of data mining on advertising, finance, astronomy, mankind, controlling crimes and many other activities which largely influences our lives.

dm2

 

End Notes

Initially, I collected 12 documentaries in total. But, after watching them, I realized these are the best ones. I’ve purposely left out some documentaries on artificial intelligence. I found them similar to these movies I shared last week. After watching them, I realized that we are the lucky ones who are living and experiencing this revolution of data. I’m sure we’ll have lot of stories to tell to our coming generations.

I hope you found these documentaries useful. My motive behind this article was to spread knowledge and awareness on importance of machine learning and statistics in an interesting way. If you are trying hard to become a data scientist, don’t lose hope. You’ve a lot on your plate to digest now.

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.

A
Analytics Vidhya

Analytics Vidhya Content team

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Responses From Readers

Clear

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despicable me hack

Undeniably consider that which you said. Your favorite justification seemed to be at the internet the easiest thing to be aware of. I say to you, I definitely get irked whilst other folks think about concerns that they plainly don't realize about. You controlled to hit the nail upon the highest and also outlined out the whole thing without having side-effects , people could take a signal. Will likely be back to get more. Thanks

123

Ayush Mehta
Ayush Mehta

Oh fuck this was hilarious.

123
Pierre
Pierre

Great post! The documentaries were very inspiring. Perhaps a part 2 soon? :)

123

karthikeya BVS
karthikeya BVS

worth to watch......great and inspiring!!

123

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