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

SGD 12 | 599
SGD 40

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Lectures
105
Language
English
Students
19
Reviews
5 (1)
Category
Business
Sub-Category
Data Analytics

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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-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. 

This course is a down-to-earth, shy but confident take on machine learning techniques that you can put to work today

Let’s parse that.

The course is down-to-earth : it makes everything as simple as possible - but not simpler

The course is shy but confident : It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff.

You can put ML to work today : If Machine Learning is a car, this car will have you driving today. It won't tell you what the carburetor is.

The course is very visual : most of the techniques are explained with the help of animations to help you understand better.

This course is practical as well : There are hundreds of lines of source code with comments that can be used directly to implement natural language processing and machine learning for text summarization, text classification in Python.

The course is also quirky. The examples are irreverent. Lots of little touches: repetition, zooming out so we remember the big picture, active learning with plenty of quizzes. There’s also a peppy soundtrack, and art - all shown by studies to improve cognition and recall.

What's Covered:

Machine Learning: 

Supervised/Unsupervised learning, Classification, Clustering, Association Detection, Anomaly Detection, Dimensionality Reduction, Regression.

Naive Bayes, K-nearest neighbours, Support Vector Machines, Artificial Neural Networks, K-means, Hierarchical clustering, Principal Components Analysis, Linear regression, Logistics regression, Random variables, Bayes theorem, Bias-variance tradeoff

Natural Language Processing with Python: 

Corpora, stopwords, sentence and word parsing, auto-summarization, sentiment analysis (as a special case of classification), TF-IDF, Document Distance, Text summarization, Text classification with Naive Bayes and K-Nearest Neighbours and Clustering with K-Means

Sentiment Analysis: 

Why it's useful, Approaches to solving - Rule-Based , ML-Based , Training , Feature Extraction, Sentiment Lexicons, Regular Expressions, Twitter API, Sentiment Analysis of Tweets with Python

A Note on Python: The code-alongs in this class all use Python 2.7. Source code (with copious amounts of comments) is attached as a resource with all the code-alongs. The source code has been provided for both Python 2 and Python 3 wherever possible.

What am I going to get from this course?

  • Identify situations that call for the use of Machine Learning
  • Understand which type of Machine learning problem you are solving and choose the appropriate solution
  • Use Machine Learning and Natural Language processing to solve problems like text classification, text summarization in Python

 

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.

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 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
Curriculum
Section 1 - Introduction
      1 : What this course is about
    Section 2 - Jump right in : Machine learning for Spam detection
        2 : Machine Learning: Why should you jump on the bandwagon?
        3 : Plunging In - Machine Learning Approaches to Spam Detection
        4 : Spam Detection with Machine Learning Continued
        5 : Get the Lay of the Land : Types of Machine Learning Problems
        6 : DOWNLOADS FOR SECTION 2
      Section 3 - Naive Bayes Classifier
          7 : Random Variables
          8 : Bayes Theorem
          9 : Naive Bayes Classifier
          10 : Naive Bayes Classifier : An example
          11 : Downloads for Section 3
        Section 4 - K-Nearest Neighbors
            12 : K-Nearest Neighbors
            13 : K-Nearest Neighbors : A few wrinkles
            14 : Downloads for Section 4
          Section 5 - Support Vector Machines
              15 : Support Vector Machines Introduced
              16 : Support Vector Machines : Maximum Margin Hyperplane and Kernel Trick
              17 : Downloads for Section 5
            Section 6 - Clustering as a form of Unsupervised learning
                18 : Clustering : Introduction
                19 : Clustering : K-Means and DBSCAN
                20 : Downloads for Section 6
              Section 7 - Association Detection
                  21 : Association Rules Learning
                  22 : Downloads Section 7
                Section 8 - Dimensionality Reduction
                    23 : Dimensionality Reduction
                    24 : Principal Component Analysis
                    25 : Downloads Section 8
                  Section 9 - Artificial Neural Networks
                      26 : Artificial Neural Networks:Perceptrons Introduced
                      27 : Downloads for Section 9
                    Section 10 - Regression as a form of supervised learning
                        28 : Regression Introduced : Linear and Logistic Regression
                        29 : Bias Variance Trade-off
                        30 : Downloads for Section 10
                      Section 11 - Natural Language Processing and Python
                          31 : Installing Python - Anaconda and Pip
                          32 : Natural Language Processing with NLTK
                          33 : Natural Language Processing with NLTK - See it in action
                          34 : Web Scraping with BeautifulSoup
                          35 : A Serious NLP Application : Text Auto Summarization using Python
                          36 : Python Drill : Autosummarize News Articles I
                          37 : Python Drill : Autosummarize News Articles II
                          38 : Python Drill : Autosummarize News Articles III
                          39 : Put it to work : News Article Classification using K-Nearest Neighbors
                          40 : Put it to work : News Article Classification using Naive Bayes Classifier
                          41 : Python Drill : Scraping News Websites
                          42 : Python Drill : Feature Extraction with NLTK
                          43 : Python Drill : Classification with KNN
                          44 : Python Drill : Classification with Naive Bayes
                          45 : Document Distance using TF-IDF
                          46 : Put it to work : News Article Clustering with K-Means and TF-IDF
                          47 : Python Drill : Clustering with K Means
                          48 : Downloads for Section 11
                        Section 12 - Sentiment Analysis
                            49 : A Sneak Peek at what's coming up02:36
                            50 : Sentiment Analysis - What's all the fuss about?
                            51 : ML Solutions for Sentiment Analysis - the devil is in the details
                            52 : Sentiment Lexicons ( with an introduction to WordNet and SentiWordNet)
                            53 : Regular Expressions
                            54 : Regular Expressions in Python
                            55 : Put it to work : Twitter Sentiment Analysis
                            56 : Twitter Sentiment Analysis - Work the API
                            57 : Twitter Sentiment Analysis - Regular Expressions for Preprocessing
                            58 : Twitter Sentiment Analysis - Naive Bayes, SVM and Sentiwordnet19:40
                            59 : Downloads for Section 12
                          Section 13 - Decision Trees
                              60 : Planting the seed - What are Decision Trees?
                              61 : Growing the Tree - Decision Tree Learning
                              62 : Branching out - Information Gain
                              63 : Decision Tree Algorithms
                              64 : Titanic : Decision Trees predict Survival (Kaggle) - I
                              65 : Titanic : Decision Trees predict Survival (Kaggle) - II
                              66 : Titanic : Decision Trees predict Survival (Kaggle) - III
                              67 : Downloads for Section 13
                            Section 14 - A Few Useful Things to Know About Overfitting
                                68 : Overfitting - the bane of Machine Learning
                                69 : Overfitting Continued
                                70 : Cross Validation
                                71 : Simplicity is a virtue - Regularization
                                72 : The Wisdom of Crowds - Ensemble Learning
                                73 : Ensemble Learning continued - Bagging, Boosting and Stacking
                                74 : Downloads for Section 14
                              Section 15 - Random Forests
                                  75 : Random Forests - Much more than trees
                                  76 : Back on the Titanic - Cross Validation and Random Forests
                                  77 : Downloads for Section 15
                                Section 16 - Recommendation Systems
                                    78 : What do Amazon and Netflix have in common?
                                    79 : Recommendation Engines - A look inside
                                    80 : What are you made of? - Content-Based Filtering
                                    81 : With a little help from friends - Collaborative Filtering
                                    82 : A Neighbourhood Model for Collaborative Filtering
                                    83 : Top Picks for You! - Recommendations with Neighbourhood Models
                                    84 : Discover the Underlying Truth - Latent Factor Collaborative Filtering
                                    85 : Latent Factor Collaborative Filtering contd
                                    86 : Gray Sheep and Shillings - Challenges with Collaborative Filtering
                                    87 : The Apriori Algorithm for Association Rules
                                    88 : Downloads for Section 16
                                  Section 17 - Recommendation Systems in Python
                                      89 : Back to Basics : Numpy in Python
                                      90 : Back to Basics : Numpy and Scipy in Python
                                      91 : Movielens and Pandas
                                      92 : Code Along - What's my favorite movie? - Data Analysis with Pandas
                                      93 : Code Along - Movie Recommendation with Nearest Neighbour CF
                                      94 : Code Along - Top Movie Picks (Nearest Neighbour CF)
                                      95 : Code Along - Movie Recommendations with Matrix Factorization
                                      96 : Code Along - Association Rules with the Apriori Algorithm
                                      97 : Downloads for Section 17
                                    Section 18 - A Taste of Deep Learning and Computer Vision
                                        98 : Computer Vision - An Introduction
                                        99 : Perceptron Revisited
                                        100 : Deep Learning Networks Introduced
                                        101 : Code Along - Handwritten Digit Recognition -I
                                        102 : Code Along - Handwritten Digit Recognition - II
                                        103 : Code Along - Handwritten Digit Recognition - III
                                        104 : Downloads for Section 18
                                      Section 19 - QUIZ
                                          105 : Quiz

                                      Reviews

Instructor :

Loonycorn A 4-ppl team;ex-Google.

Biography

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 :-)

Reviews

Average Rating
 (1 Reviews)

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Donanld Mey

posted 2 month before

Excellent Course for beginners

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.