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

From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase, available at $59.99, has an average rating of 4.45, with 95 lectures, 27 quizzes, based on 903 reviews, and has 8754 subscribers.

You will learn about 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 This course is ideal for individuals who are Yep! Analytics professionals, modelers, big data professionals who haven't had exposure to machine learning or Yep! Engineers who want to understand or learn machine learning and apply it to problems they are solving or Yep! Product managers who want to have intelligent conversations with data scientists and engineers about machine learning or Yep! Tech executives and investors who are interested in big data, machine learning or natural language processing or Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role It is particularly useful for Yep! Analytics professionals, modelers, big data professionals who haven't had exposure to machine learning or Yep! Engineers who want to understand or learn machine learning and apply it to problems they are solving or Yep! Product managers who want to have intelligent conversations with data scientists and engineers about machine learning or Yep! Tech executives and investors who are interested in big data, machine learning or natural language processing or Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role .

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

Summary

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

Price: $59.99

Average Rating: 4.45

Number of Lectures: 95

Number of Quizzes: 27

Number of Published Lectures: 94

Number of Published Quizzes: 27

Number of Curriculum Items: 122

Number of Published Curriculum Objects: 121

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: You, This Course and Us

Lecture 2: Source Code and PDFs

Lecture 3: A sneak peek at what's coming up

Chapter 2: Jump right in : Machine learning for Spam detection

Lecture 1: Solving problems with computers

Lecture 2: Machine Learning: Why should you jump on the bandwagon?

Lecture 3: Plunging In – Machine Learning Approaches to Spam Detection

Lecture 4: Spam Detection with Machine Learning Continued

Lecture 5: Get the Lay of the Land : Types of Machine Learning Problems

Chapter 3: Solving Classification Problems

Lecture 1: Solving Classification Problems

Lecture 2: Random Variables

Lecture 3: Bayes Theorem

Lecture 4: Naive Bayes Classifier

Lecture 5: Naive Bayes Classifier : An example

Lecture 6: K-Nearest Neighbors

Lecture 7: K-Nearest Neighbors : A few wrinkles

Lecture 8: Support Vector Machines Introduced

Lecture 9: Support Vector Machines : Maximum Margin Hyperplane and Kernel Trick

Lecture 10: Artificial Neural Networks:Perceptrons Introduced

Chapter 4: Clustering as a form of Unsupervised learning

Lecture 1: Clustering : Introduction

Lecture 2: Clustering : K-Means and DBSCAN

Chapter 5: Association Detection

Lecture 1: Association Rules Learning

Chapter 6: Dimensionality Reduction

Lecture 1: Dimensionality Reduction

Lecture 2: Principal Component Analysis

Chapter 7: Regression as a form of supervised learning

Lecture 1: Regression Introduced : Linear and Logistic Regression

Lecture 2: Bias Variance Trade-off

Chapter 8: Natural Language Processing and Python

Lecture 1: Applying ML to Natural Language Processing

Lecture 2: Installing Python – Anaconda and Pip

Lecture 3: Natural Language Processing with NLTK

Lecture 4: Natural Language Processing with NLTK – See it in action

Lecture 5: Web Scraping with BeautifulSoup

Lecture 6: A Serious NLP Application : Text Auto Summarization using Python

Lecture 7: Python Drill : Autosummarize News Articles I

Lecture 8: Python Drill : Autosummarize News Articles II

Lecture 9: Python Drill : Autosummarize News Articles III

Lecture 10: Put it to work : News Article Classification using K-Nearest Neighbors

Lecture 11: Put it to work : News Article Classification using Naive Bayes Classifier

Lecture 12: Python Drill : Scraping News Websites

Lecture 13: Python Drill : Feature Extraction with NLTK

Lecture 14: Python Drill : Classification with KNN

Lecture 15: Python Drill : Classification with Naive Bayes

Lecture 16: Document Distance using TF-IDF

Lecture 17: Put it to work : News Article Clustering with K-Means and TF-IDF

Lecture 18: Python Drill : Clustering with K Means

Chapter 9: Sentiment Analysis

Lecture 1: Solve Sentiment Analysis using Machine Learning

Lecture 2: Sentiment Analysis – What's all the fuss about?

Lecture 3: ML Solutions for Sentiment Analysis – the devil is in the details

Lecture 4: Sentiment Lexicons ( with an introduction to WordNet and SentiWordNet)

Lecture 5: Regular Expressions

Lecture 6: Regular Expressions in Python

Lecture 7: Put it to work : Twitter Sentiment Analysis

Lecture 8: Twitter Sentiment Analysis – Work the API

Lecture 9: Twitter Sentiment Analysis – Regular Expressions for Preprocessing

Lecture 10: Twitter Sentiment Analysis – Naive Bayes, SVM and Sentiwordnet

Chapter 10: Decision Trees

Lecture 1: Using Tree Based Models for Classification

Lecture 2: Planting the seed – What are Decision Trees?

Lecture 3: Growing the Tree – Decision Tree Learning

Lecture 4: Branching out – Information Gain

Lecture 5: Decision Tree Algorithms

Lecture 6: Titanic : Decision Trees predict Survival (Kaggle) – I

Lecture 7: Titanic : Decision Trees predict Survival (Kaggle) – II

Lecture 8: Titanic : Decision Trees predict Survival (Kaggle) – III

Chapter 11: A Few Useful Things to Know About Overfitting

Lecture 1: Overfitting – the bane of Machine Learning

Lecture 2: Overfitting Continued

Lecture 3: Cross Validation

Lecture 4: Simplicity is a virtue – Regularization

Lecture 5: The Wisdom of Crowds – Ensemble Learning

Lecture 6: Ensemble Learning continued – Bagging, Boosting and Stacking

Chapter 12: Random Forests

Lecture 1: Random Forests – Much more than trees

Lecture 2: Back on the Titanic – Cross Validation and Random Forests

Chapter 13: Recommendation Systems

Lecture 1: Solving Recommendation Problems

Lecture 2: What do Amazon and Netflix have in common?

Lecture 3: Recommendation Engines – A look inside

Lecture 4: What are you made of? – Content-Based Filtering

Lecture 5: With a little help from friends – Collaborative Filtering

Lecture 6: A Neighbourhood Model for Collaborative Filtering

Lecture 7: Top Picks for You! – Recommendations with Neighbourhood Models

Lecture 8: Discover the Underlying Truth – Latent Factor Collaborative Filtering

Lecture 9: Latent Factor Collaborative Filtering contd.

Lecture 10: Gray Sheep and Shillings – Challenges with Collaborative Filtering

Lecture 11: The Apriori Algorithm for Association Rules

Chapter 14: Recommendation Systems in Python

Lecture 1: Back to Basics : Numpy in Python

Lecture 2: Back to Basics : Numpy and Scipy in Python

Lecture 3: Movielens and Pandas

Lecture 4: Code Along – What's my favorite movie? – Data Analysis with Pandas

Lecture 5: Code Along – Movie Recommendation with Nearest Neighbour CF

Lecture 6: Code Along – Top Movie Picks (Nearest Neighbour CF)

Instructors

Loony Corn
An ex-Google, Stanford and Flipkart team

Rating Distribution

1 stars: 30 votes
2 stars: 36 votes
3 stars: 136 votes
4 stars: 282 votes
5 stars: 419 votes

Frequently Asked Questions

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