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Development Data Science Machine Learning

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

A down-to-earth, shy but confident take on machine learning techniques that you can put to work today
Rating: 4.2 out of 54.2 (890 ratings)
8,663 students
Created by Loony Corn
Last updated 1/2018
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • 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

Course content

16 sections • 94 lectures • 19h 50m total length

  • Preview02:24
  • Source Code and PDFs
    00:04
  • A sneak peek at what's coming up
    04:12

  • Solving problems with computers
    02:11
  • Preview07:28
  • 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

  • Solving Classification Problems
    00:59
  • Random Variables
    11:27
  • Bayes Theorem
    11:55
  • Naive Bayes Classifier
    05:26
  • Naive Bayes Classifier : An example
    Preview09:18
  • K-Nearest Neighbors
    13:09
  • K-Nearest Neighbors : A few wrinkles
    14:47
  • Support Vector Machines Introduced
    08:16
  • Support Vector Machines : Maximum Margin Hyperplane and Kernel Trick
    16:23
  • Artificial Neural Networks:Perceptrons Introduced
    11:18

  • Clustering : Introduction
    Preview19:07
  • Clustering : K-Means and DBSCAN
    13:42

  • Association Rules Learning
    09:12

  • Dimensionality Reduction
    10:22
  • Principal Component Analysis
    18:53

  • Regression Introduced : Linear and Logistic Regression
    13:54
  • Bias Variance Trade-off
    Preview10:13

  • Applying ML to Natural Language Processing
    00:56
  • Installing Python - Anaconda and Pip
    09:00
  • Preview07:26
  • Natural Language Processing with NLTK - See it in action
    14:14
  • Web Scraping with BeautifulSoup
    18:08
  • 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

  • Solve Sentiment Analysis using Machine Learning
    02:36
  • 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
  • Preview12:24
  • Twitter Sentiment Analysis - Naive Bayes, SVM and Sentiwordnet
    19:40

  • Using Tree Based Models for Classification
    01:00
  • Planting the seed - What are Decision Trees?
    Preview17:01
  • 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

Requirements

  • 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.

Description

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

Mitigating Overfitting with Ensemble Learning:

Decision trees and decision tree learning, Overfitting in decision trees, Techniques to mitigate overfitting (cross validation, regularization), Ensemble learning and Random forests

Recommendations:  Content based filtering, Collaborative filtering and Association Rules learning

Get started with Deep learning: Apply Multi-layer perceptrons to the MNIST Digit recognition problem

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.

Who this course is for:

  • 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

Instructor

Loony Corn
An ex-Google, Stanford and Flipkart team
Loony Corn
  • 4.2 Instructor Rating
  • 22,020 Reviews
  • 131,346 Students
  • 73 Courses

Loonycorn is us, Janani Ravi and Vitthal Srinivasan. Between us, we have studied at Stanford, been admitted to IIM Ahmedabad and have spent years  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

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 Udemy!

We hope you will try our offerings, and think you'll like them :-)

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