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From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase

By: Loonycorn A 4-Ppl Team;ex-Google.

  • 4
  • (73)
  • 17:54:55
  • 105
  • 392
  • Language: English
449 1996
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Course Summary

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.

Taught by a Stanford-educated, ex-

Read More

Target Audience

  • Yep! Analytics professionals, modelers, big data professionals who haven't had exposure to machine learning
  • Yep! Engineers who want to understand or learn python machine learning and apply it to problems they are solving
  • Yep! Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
  • Yep! Tech executives and investors who are interested in big data, machine learning or natural language processing
  • Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role

Pre-Requisites

  • No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.

Curriculum

  • What this course is about
    02:24
  • DOWNLOADS FOR SECTION 2
  • Machine Learning: Why should you jump on the bandwagon?
    07:28
  • Plunging In - Machine Learning Approaches to Spam Detection
    11:48
  • Spam Detection with Machine Learning Continued
    11:07
  • Get the Lay of the Land : Types of Machine Learning Problems
    09:45
  • Downloads for Section 3
  • Random Variables
    11:27
  • Bayes Theorem
    11:55
  • Naive Bayes Classifier
    05:26
  • Naive Bayes Classifier : An example
    09:18
  • Downloads for Section 4
  • K-Nearest Neighbors
    13:09
  • K-Nearest Neighbors : A few wrinkles
    14:47
  • Downloads for Section 5
  • Support Vector Machines Introduced
    08:16
  • Support Vector Machines : Maximum Margin Hyperplane and Kernel Trick
    16:23
  • Downloads for Section 6
  • Clustering : Introduction
    19:07
  • Clustering : K-Means and DBSCAN
    13:42
  • Downloads for Section 7
  • Association Rules Learning
    09:12
  • Downloads for Section 8
  • Dimensionality Reduction
    10:22
  • Principal Component Analysis
    18:53
  • Downloads for Section 9
    00:00
  • Artificial Neural Networks:Perceptrons Introduced
    00:00
  • Downloads for Section 10
    00:00
  • Regression Introduced : Linear and Logistic Regression
    13:54
  • Bias Variance Trade-off
    10:13
  • Downloads for Section 11
    00:00
  • Installing Python - Anaconda and Pip
    09:00
  • Natural Language Processing with NLTK
    07:26
  • Natural Language Processing with NLTK - See it in action
    14:14
  • Web Scraping with BeautifulSoup
    18:18
  • A Serious NLP Application : Text Auto Summarization using Python
    11:34
  • Python Drill : Autosummarize News Articles I
    18:33
  • Python Drill : Autosummarize News Articles II
    11:28
  • Python Drill : Autosummarize News Articles III
    10:23
  • Put it to work : News Article Classification using K-Nearest Neighbors
    19:29
  • Put it to work : News Article Classification using Naive Bayes Classifier
    19:24
  • Python Drill : Scraping News Websites
    15:45
  • Python Drill : Feature Extraction with NLTK
    18:51
  • Python Drill : Classification with KNN
    04:15
  • Python Drill : Classification with Naive Bayes
    08:08
  • Document Distance using TF-IDF
    11:03
  • Put it to work : News Article Clustering with K-Means and TF-IDF
    14:32
  • Python Drill : Clustering with K Means
    08:32
  • Downloads for Section 12
    00:00
  • A Sneak Peek at what's coming up
    00:00
  • Sentiment Analysis - What's all the fuss about?
    17:17
  • ML Solutions for Sentiment Analysis - the devil is in the details
    19:57
  • Sentiment Lexicons ( with an introduction to WordNet and SentiWordNet)
    18:49
  • Regular Expressions
    17:53
  • Regular Expressions in Python
    05:41
  • Put it to work : Twitter Sentiment Analysis
    17:48
  • Twitter Sentiment Analysis - Work the API
    20:00
  • Twitter Sentiment Analysis - Regular Expressions for Preprocessing
    12:24
  • Twitter Sentiment Analysis - Naive Bayes, SVM and Sentiwordnet
    19:40
  • Downloads for Section 13
    00:00
  • Planting the seed - What are Decision Trees?
    17:01
  • Growing the Tree - Decision Tree Learning
    18:03
  • Branching out - Information Gain
    18:51
  • Decision Tree Algorithms
    07:50
  • Titanic : Decision Trees predict Survival (Kaggle) - I
    19:21
  • Titanic : Decision Trees predict Survival (Kaggle) - II
    14:16
  • Titanic : Decision Trees predict Survival (Kaggle) - III
    13:00
  • Downloads for Section 16
    00:00
  • Overfitting - the bane of Machine Learning
    00:00
  • Overfitting Continued
    00:00
  • Cross Validation
    00:00
  • Simplicity is a virtue - Regularization
    00:00
  • The Wisdom of Crowds - Ensemble Learning
    00:00
  • Ensemble Learning continued - Bagging, Boosting and Stacking
    00:00
  • Downloads for Section 14
    00:00
  • Random Forests - Much more than trees
    12:28
  • Back on the Titanic - Cross Validation and Random Forests
    20:03
  • Downloads for Section 15
    00:00
  • What do Amazon and Netflix have in common?
    16:43
  • Recommendation Engines - A look inside
    10:45
  • What are you made of? - Content-Based Filtering
    13:35
  • With a little help from friends - Collaborative Filtering
    10:26
  • A Neighbourhood Model for Collaborative Filtering
    17:50
  • Top Picks for You! - Recommendations with Neighbourhood Models
    09:41
  • Discover the Underlying Truth - Latent Factor Collaborative Filtering
    20:13
  • Latent Factor Collaborative Filtering contd
    12:09
  • Gray Sheep and Shillings - Challenges with Collaborative Filtering
    08:12
  • The Apriori Algorithm for Association Rules
    18:31
  • Downloads for Section 17
    00:00
  • Back to Basics : Numpy in Python
    18:05
  • Back to Basics : Numpy and Scipy in Python
    14:19
  • Movielens and Pandas
    16:45
  • Code Along - What's my favorite movie? - Data Analysis with Pandas
    06:18
  • Code Along - Movie Recommendation with Nearest Neighbour CF
    18:10
  • Code Along - Top Movie Picks (Nearest Neighbour CF)
    06:16
  • Code Along - Movie Recommendations with Matrix Factorization
    17:55
  • Code Along - Association Rules with the Apriori Algorithm
    09:50
  • Downloads for Section 18
    00:00
  • Computer Vision - An Introduction
    18:09
  • Perceptron Revisited
    16:01
  • Deep Learning Networks Introduced
    17:01
  • Code Along - Handwritten Digit Recognition -I
    14:29
  • Code Along - Handwritten Digit Recognition - II
    17:37
  • Code Along - Handwritten Digit Recognition - III
    06:02
  • Quiz
    00:00

About the Author

Loonycorn A 4-Ppl Team;ex-Google.,

Loonycorn is us, Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh. Between the four of us, we have studied at Stanford, IIM Ahmedabad, the IITs and have spent years (decades, actually) working in tech, in the Bay Area, New York, Singapore and Bangalore. Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum Navdeep: longtime Flipkart employee too, and IIT Guwahati alum We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Unanth! We hope you will try our offerings, and think you'll like them :-)

More From Author

Reviews

Donanld Mey
5

I really enjoyed this course. 1. Each topic is very clear explained 2. explanations are very well illustrated 3. hands on code is provided 4. very good examples.

Gaurav Harsola
5

Covered lots of content..Teaching Style is Good..Easy to understand..

Raj Mudigonda
5

Totally worth it.One thing I liked about this course is concepts are explained in very understandable way and to the point without wasting time.

Sameer Kumar
3

Course is good but please stop the annoying background music.

Uddyan Sinha
5

Awesome way of learning

Shiva Msk
2

Content is pretty good but it would have been great if there is an option in the video to increase the speed to 1.25x ,1.5x or 2x similar to youtube videos.Could you guys please do that

Vikhyat Prabhu
4

The course does not remember the last watched video and after sometime keeps asking for sign in. Course content looks good till now

Rahul Menon
4

Hi I liked the course structure and content , explained in a easy way. Keep up the good job. One suggestion is if you can improve the quality of text that appears in formulae or charts , it very difficult to read those

From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase

  • 17:54:55
  • 105
  • 392
  • Language: English
1996 449
  • 15 days Money back Gurantee
  • Unlimited Access
  • Android, iPhone and iPad Access
  • Certificate of Completion

Course Summary

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.

Taught by a Stanford-educated, ex-

Read More

Target Audience

  • Yep! Analytics professionals, modelers, big data professionals who haven't had exposure to machine learning
  • Yep! Engineers who want to understand or learn python machine learning and apply it to problems they are solving
  • Yep! Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
  • Yep! Tech executives and investors who are interested in big data, machine learning or natural language processing
  • Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role

Pre-Requisites

  • Yep! Analytics professionals, modelers, big data professionals who haven't had exposure to machine learning
  • Yep! Engineers who want to understand or learn python machine learning and apply it to problems they are solving
  • Yep! Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
  • Yep! Tech executives and investors who are interested in big data, machine learning or natural language processing
  • Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role

About the Author

Loonycorn A 4-Ppl Team;ex-Google.,

Loonycorn is us, Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh. Between the four of us, we have studied at Stanford, IIM Ahmedabad, the IITs and have spent years (decades, actually) working in tech, in the Bay Area, New York, Singapore and Bangalore. Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum Navdeep: longtime Flipkart employee too, and IIT Guwahati alum We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Unanth! We hope you will try our offerings, and think you'll like them :-)

More From Author

Review & Rating

Donanld Mey 5

I really enjoyed this course. 1. Each topic is very clear explained 2. explanations are very well illustrated 3. hands on code is provided 4. very good examples.

Gaurav Harsola 5

Covered lots of content..Teaching Style is Good..Easy to understand..

Raj Mudigonda 5

Totally worth it.One thing I liked about this course is concepts are explained in very understandable way and to the point without wasting time.

Sameer Kumar 3

Course is good but please stop the annoying background music.

Uddyan Sinha 5

Awesome way of learning

Shiva Msk 2

Content is pretty good but it would have been great if there is an option in the video to increase the speed to 1.25x ,1.5x or 2x similar to youtube videos.Could you guys please do that

Vikhyat Prabhu 4

The course does not remember the last watched video and after sometime keeps asking for sign in. Course content looks good till now

Rahul Menon 4

Hi I liked the course structure and content , explained in a easy way. Keep up the good job. One suggestion is if you can improve the quality of text that appears in formulae or charts , it very difficult to read those