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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

27 Top Videos, Tutorials & Courses on Machine Learning & Artificial Intelligence from 2016

A
Analytics Vidhya Last Updated : 08 Jul, 2020
12 min read

Introduction

2016 has been the year of “Machine Learning and Deep Learning”. We have seen the likes of Google, Facebook, Amazon and many more come out in open and acknowledge the impact machine learning and deep learning had on their business.

Last week, I published top videos on deep learning from 2016. I was blown away by the response. I could understand the response to some degree – I found these videos extremely helpful. So, I decided to do a similar article on top videos on machine learning from 2016.

 

How to decide your own learning path?

People often ask us “How should I get started with data science & machine learning?”

There can not be just one answer to this question. The anatomy of machine learning is quite vast. One needs to decide their own framework and time period to get comfortable with machine learning. Through this article, I want to help you reach that comfort zone.

In this article, I have compiled popular and most viewed machine learning videos, tutorials and courses from 2016. I want to help you to get started with machine learning and gain expertise building predictive models using machine learning. You are free to decide your own framework and time period to watch these videos. It is always advisable to take baby steps but feel free to mould it as per your wish.

In order to help you, I have arranged the article and suggested a way to go through them. Hope you find it useful.

 

How to use this article?

In this article, I have segregated the videos for beginner & advanced. The first section of the article will introduce you to machine learning basics and the underlying theory to make you get comfortable with the machine learning. If you are just getting started with machine learning then this section should be your first stop. Go through these videos one by one, and put the learning into practice with a practice problem.

For those who already have a basic understanding of machine learning, you should start with the advance machine learning videos. These videos will introduce you to various machine learning libraries, modeling techniques and other advanced concepts of machine learning. Once you are thorough understanding with advanced concepts of machine learning, try your hands on Black Friday practice problem.

After you have an understanding of how machine learning works, embark upon the next section on the applications of machine learning. These videos will blow your mind on how machine learning is changing the world. Find out how companies today are using machine learning to makes things simpler for us.

 

Table of Contents

  1. Machine Learning for Beginners
  2. Machine Learning: Advanced
  3. Application of Machine Learning

 

Machine Learning For Beginners

1.How to become a Data Scientist in 6 months

Duration: 56:24 mins

In this video, Tetiana Ivanova shares her journey of becoming a data scientist in just 6 months. Participating in hackathons got her started with machine learning. If you have been wondering whether to go for analytics post-graduate program or become self-taught data science professional, you must watch this video. Tetiana shares her real life experience of making the career move, the hardships and truth behind the facade of a higher education. Either you are a beginner or someone transitioning his / her career to data science then I would recommend that you must watch this video. This video will leave you inspired.

 

2. Statistical Machine Learning Course

Duration: N/A

In this course from Carnegie Mellon University, it will take you through basics of machine learning and statistical modeling. You will learn about parametric &  non-parametric regression, clustering, boosting, graphical methods, minimax theory, dimensionality reduction, etc. This course is best suited for students with a sound background in statistics & mathematics. Alongside, there are assignments & solution which would further improve your concepts. Feel free to skip the initial few minutes of the first video.

 

3. Machine Learning Course 

Duration: N/A

This course on machine learning from the University of Waterloo will take you through machine learning basics and advanced concepts. It’s a conceptual course which will educate you on mathematical relations in ML algorithms. It has been taught by multiple professors including Shai Ben David, author of book Understanding machine learning. It covers topics such as linear regression, bayesian, trees, clustering, neural networks, ensemble, hidden markov model and much more. Check out the other course material here. Feel free to skip the first 8 mins of the video.

 

4. Practical Machine Learning Tutorial with Python

Duration: N/A

This course is designed for all the Python practitioners looking for a comprehensive introduction to machine learning. It covers theoretical & practical concepts on supervised, unsupervised and deep learning algorithms. In this series of videos, you will learn about linear regression, K-Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks. For each algorithm, the speaker discusses the real-life applications with the help of actual datasets. Then you will learn about the workings of each of the algorithms by recreating them in code. This will provide you a complete understanding of exactly how the algorithms work, how they can be tweaked to our advantage.

 

5. Introduction – Learn Python for Data Science 

Duration: N/A

Here’s yet another tutorial to learn Python for data science. If your hectic work schedule has been making it difficult for you to start with learning data science then these videos are at your rescue. In these 7 min videos, you will learn how to get started with data science. It will introduce you to sentimental analysis, recommendation system, predicting stock prices, create neural network using python & tensorflow and introduction to genetic algorithms. The speaker is clear in his approach and will let you learn data science in your tea breaks. This tutorial requires basic understanding of Python.

 

6. Machine Learning Tutorial 

Duration: 3 hours

This is an exclusive tutorial by Sebastian Raschka and Andreas Muller from the SciPy Conference held in July 2016. In this tutorial, Sebastian introduces machine learning & Scikit Learn with sample applications. Then he will go on to explain the different computational tools for Python: NumPy, SciPy and matplotlib. Sebastian explains data representation using Iris dataset for machine learning. Andreas introduces you to classification and regression techniques in supervised learning. Sebastian then explains clustering technique for unsupervised learning and will make you familiar with the interface of scikit-learn, one of the widely used python libraries. It will also provide you hands-on experience in building a predictive model using Titanic dataset.

 

7. Data Analysis in Python with Pandas

Duration: N/A

Pandas is full-featured Python library for data analysis, manipulation and visualization. With high its readability and general purpose use, Python is often a popular choice for beginners to start with data science. This tutorial is for Python users who want to understand the vast data and get started with data science. The series will introduce you to Pandas and what all you can do using Pandas library. In this 31 video series, the speaker will take you through each step of Pandas involved in data analysis.

 

8. Machine Learning – CS50 2016

Duration: 1 hour 30 mins

This is a video from CS50 course taught at Harvard and Yale University. In this video, the speaker introduces you to machine learning and its applications. For all coders out there this is one of the best tutorials for you to get started with machine learning using Python. It is a simple introduction to machine learning and it is affecting our lives today. Learn how machine learning is being applied for building search engines, image recognition, voice recognition, and natural language processing. This tutorial will teach you image classification with Python and text clustering. Skip the first 9:05 mins of the video.

 

9. Analyzing and Manipulating Data with Pandas Beginner

Duration: 3 hour 30 mins

As I described above, Pandas is one of the popular libraries in Python. This tutorial will take you through analyzing and manipulating data in Python using Pandas. Pandas ecosystem is growing more & more and it user friendliness makes data analysis simpler. This tutorial is aimed at any beginner who wants to get started with data analysis in Python. It uses climate datasets to learn about Pandas.

 

10.What is Artificial Intelligence

Duration: 9:21 mins

Artificial Intelligence is a means to make machines smart enough to take actions on their own. There is a lot of buzz around AI but people often ask the question what is AI exactly? Here is a brief video which takes you to the origin of AI. Learn how AI has evolved into a mainstream topic and how its various applications are changing the world. AI has created a possibility for machines to differentiate between a dog and a human. Learn what are expert systems and how image recognition, robotics, deep learning are all inter-connected with AI.

Machine Learning: Advanced

1. Machine Learning Recipes

Duration: N/A

Machine learning is making systems so smart that they are getting closer & closer to replacing humans. In this these 10 min videos you will about the various applications of machine learning. By watching the first video itself you will be able to write your first code. In these videos you will learn about decision tree visualization, scikit- learn, tensorflow, how to build your own classifier, what is the most accurate features for your model and many more interesting concepts. The language used is Python. The videos are very informative and a must watch for any intermediate in data science.

 

2. Machine Learning with Text in Scikit-Learn

Duration: 2 hours 40 mins

Although numeric data is easy to work with in Python, most knowledge created by humans is actually raw, unstructured text. By learning how to transform text into data that is usable by machine learning models, you drastically increase the amount of data that your models can learn from. In this tutorial, you will learn to build and evaluate predictive models from real-world text using scikit-learn. By the end of this tutorial, you will be able to confidently build predictive models from text-based data along with feature extraction, model building and model evaluation. This tutorial was delivered in PyCon 2016.

 

3. Machine Learning for Hackers

Duration: N/A

Ever wondered how Netflix recommends shows based on your choice or how Amazon recommends you products they think you might also like. For any machine learning practitioner, these questions are no brainer. In this tutorial, the speaker introduces you to machine learning and how it is being used to solve various problems, build AI based games and many other application of ML. Well not just an introduction to these applications but you will learn how to build movie recommender system, chatbots, AI game and AI reader & writer. There are 5 min videos with key takeaways. These videos are meant for any machine learning hacker and requires one to have thorough understanding of machine learning concepts.

 

4.Introduction to Machine Learning on Apache Spark MLlib

Duration: 42:19 mins

Spark MLlib is a library for performing machine learning and associated tasks on massive datasets. With MLlib, fitting a machine learning model to a billion observations can take only a few lines of code. Along with this one can leverage hundreds of machines. In this tutorial, a senior data scientist from Cloudera introduces you through Apache Spark from scratch. You will learn about how Spark works and its execution model. The speaker has used several examples to explain the interactivity which Spark offers. It also covers the use of Spark’s DataFrames API for fast data manipulation, as well as ML Pipelines for making the model development and refinement process easier.

 

5. Time Series Analysis with Python Intermediate

Duration: 3 hours

In this tutorial, you will learn why should you use time series and what is the importance of time series analysis. The speaker provides a quick 10 mins introduction to Pandas to provide a refresher. Then you will see time series in action and learn how to deal with calendar dates in Pandas. The speaker will teach you how to understand the different time-stamped data like US-GIS, NIH, FRB, etc. Learn about the common time series analytical tools, prediction and classification in time series.

 

Applications of Machine Learning

1. Breakthroughs in Machine Learning

Duration: 28:26 mins

Machine Learning is producing smarter gadgets and machines. Siri and Cortona is a result of major advances in machine learning. But, what goes behind creating these products. Let’s learn that in this video from Google data science team. The team first takes you through speech recognition and how it has been made possible. Then understand how machine learning is used on graphs to make image classification and smart replies a reality. It is an interesting video which reveals all the backend operations a machine performs for three major machine learning applications developed by Google.

 

2. Machine Learning & Art

Duration: 42:35 mins

The recent progress of machine learning is impressive, and the applications seem endless. Neural networks are incredible tools allowing artist not just to analyze but also manipulate and generate images, movies and music. In this video, the speaker explains how The Google Cultural institute in finding ways to use machine learning for art and culture. In my last article on deep learning videos, we saw a video to create neural art. In this video, the speakers take you through all the fun stuff one can do with machine learning like Train Mario games, create artful collages using machine learning, create digital interactive images & videos. It is a very interesting video and I would recommend everyone to must watch it.

 

3. How computers are learning to be creative

Duration: 17:34 mins

Here is one of the amazing videos I have come across on applications of machine learning. Deep Learning is a subfield of artificial intelligence. Through deep learning, the aim of data scientists is to interpret the same functionality of a human brain into machines. In this Ted talk by Blaise Agüera y Arcas, Principal Scientist at Google he shares how machine learning algorithms and neural nets are used to build machine perception. In this video he shows how neural nets trained to recognize images can be run in reverse, to generate the same images. He explains this with several visual examples.

 

4. Machine learning to decode the genome

Duration: 9:53 mins

Machine Learning can also be applied for understanding human genome, revealing a whole new world of personalized medication. In this video, Anshul Kundaje assistant professor of genetics and of computer science at Stanford explains how machine learning can be used for this purpose. He explains how the genomes of healthy individuals can be compared with their family members diagonised with a particular disease to identify disease associated genetic variants. I think this can be a revolutionary step in detecting early chances of diseases like Alzheimers and Cancer.

5. How GrabTaxi uses machine learning to predict taxi availability

Duration: 11:24 mins

Personally, to me it is surprising to see how machine learning can solve business problems at different levels. One such example is how Grab Taxi uses machine learning to tackle the problem of taxi availability. To handle this problem, Grab started a unique initiative of bidding for a ride by the drivers and the fastest bidder wins and is assigned the ride. Watch the full video to find out how they used machine learning to build a predictive model on drivers bidding probability and used real time data to solve the problem.

 

6. Getting Started with Amazon Machine Learning

Duration: 54:43 mins

Amazon Machine Learning (Amazon ML) is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning’s powerful algorithms create machine learning models by finding patterns in your existing data. In this tutorial, you will learn how to use machine learning with the data you already have to arrive at accurate and actionable predictions, i.e, to create smart applications. You will learn how to use and integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud.

 

7. Amazon Go – world’s most advanced shopping technology

Duration: 1:49 mins

I think this is one of the most fascinating technologies that will blow off every hot & shot organized retail. Amazon is using computer vision, machine learning, deep learning algorithms and sensor fusions to give you an out of the world shopping experience. Just to be clear, I am not promoting Amazon Go but trying to show you what all machine learning can do.

8. Knowledge Graphs for a Connected World

Duration: 48:41 mins

The Connected graph is the answer to optimal business strategies and the key for growth in today’s world. The ability to utilize the connected data to understand the relationship between any user or customer. In this video, the speaker explains the graph database technology which uses AI, machine learning, and deep learning. You will learn about the basics of connected graphs and how they work. How AI forms the basis of these connected graphs and see knowledgeable connected graphs in action with popular use cases.

 

9. The AI Gaming Revolution

Duration: 10:30 mins

Artificial intelligence powered computers have come a long way. The machines are so smart today that they can beat humans in any new game. AlphaGo gained significant attention when it won against the professional human Go player. But the questions is what goes in their brain and how are they able to perform that well. In this video, you will learn about Heuristics, production systems and deep neural networks which has made AI games a reality.

 

10. Better Medicine Through Machine Learning

Duration: 17:56 mins

Each & every industry is realizing the potential of Machine Learning. In this video, the speaker a machine learning practitioner in healthcare explains how machine learning can be used to detect diseases at their early stage. The speaker suggests the same technology which is used in self-driving cars, AI powered games can be used in detecting the early signs of a disease. If you are wondering how this is possible, then it is basically by using massive data available with hospitals and analyzing the patterns within the data. Imagine how many lives can be saved if hospitals start using machine learning on regular basis.

 

11. Build Smart Application with your own Superpower

Duration: 42:32 mins

Google Photos and Google Translation is yet another example of machine learning applications. In this video from Google you will learn how developers can use machine learning at their leverage to build much powerful apps at their end. Learn how Academy Award-winning and recognized studios takes advantage of cloud economics and Google’s on-demand computing to realize their creative visions and expand this digital medium for storytelling

 

12. Machine Learning: Google’s Vision

Duration: 44:44 mins

How Google uses machine learning? Hear from the Google’s machine learning team, how they have been using machine learning to produce products one couldn’t have imagined. Learn more about Google’s take on machine learning and AI, how machine learning has streamlined Google’s end products. Also, it has deployed practical AI throughout its products and has brought an end user closer to the technology.

 

End Notes

I hope you enjoyed reading this article. Now that you know, all the popular and must videos in machine learning from 2016 tell me how are you planning to watch to them. I would also like to know your feedback on this article. Kindly, drop in your comments below and share your opinions.

If you have watched any of these videos, share your reviews with me and other users in the comments section. I hope this article was a great value add to your knowledge.

Through this article, I wanted to ensure that you’re ahead of your learning goal. If there is any particular tool or technique you would like to focus on in 2017 then tell us which are they. Just for your knowledge, we are giving away reference guides for Machine Learning, Python, R, Big-Data, Deep Learning and many more. To download these guides, click here.

You can test your skills and knowledge. Check out Live Competitions and compete with best Data Scientists from all over the world.

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Responses From Readers

Clear

Dev
Dev

Great collection, Swati! Sounds interesting!

123

Swati Kashyap
Swati Kashyap

Thanks Dev.I'm glad you found it helpful.

123
Abhi
Abhi

Thanks for sharing this Swati. it must have required good amount of efforts. I think its slightly tilted towards Pythan compared to R. I am new to Analytics and wants to learn R first, before starting with Pythan.

123

Swati Kashyap
Swati Kashyap

Hi Abhi, The intention was certainly not to select only Python related videos. But these are the top videos on machine learning on Youtube for 2016.And thanks for the appreciation. :)

123
Karthikeyan Sankaran
Karthikeyan Sankaran

Hi Swati - Great list of videos with the right blend of education & practical applications. Thank you for compiling the list. #VeryUseful

123

Swati Kashyap
Swati Kashyap

Your welcome :)

123

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