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

Preview in Mapt

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

Loonycorn

A down-to-earth, shy but confident take on machine learning techniques that you can put to work today
Mapt Subscription
FREE
$29.99/m after trial
Video
$28.05
RRP $32.99
Save 14%
What do I get with a Mapt Pro subscription?
  • Unlimited access to all Packt’s 5,000+ eBooks and Videos
  • Early Access content, Progress Tracking, and Assessments
  • 1 Free eBook or Video to download and keep every month after trial
What do I get with an eBook?
  • Download this book in EPUB, PDF, MOBI formats
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
What do I get with Print & eBook?
  • Get a paperback copy of the book delivered to you
  • Download this book in EPUB, PDF, MOBI formats
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
What do I get with a Video?
  • Download this Video course in MP4 format
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
$0.00
$28.05
$29.99 p/m after trial
RRP $32.99
Subscription
Video
Start 14 Day Trial

Frequently bought together


From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase [Video] Book Cover
From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase [Video]
$ 32.99
$ 28.05
From 0 to 1: Data Structures & Algorithms in Java [Video] Book Cover
From 0 to 1: Data Structures & Algorithms in Java [Video]
$ 32.99
$ 28.05
Buy 2 for $35.00
Save $30.98
Add to Cart

Video Details

ISBN 139781788624329
Course Length19 hours 15 minutes

Video Description

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.

Style and Approach

A 19 hour course well designed to put ML to work today.

Table of Contents

Introduction
You, This Course and Us
A sneak peek at what's coming up
Jump right in: Machine learning for Spam detection
Solving problems with computers
Machine Learning: Why should you jump on the bandwagon?
Plunging In - Machine Learning Approaches to Spam Detection
Spam Detection with Machine Learning Continued
Get the Lay of the Land: Types of Machine Learning Problems
Solving Classification Problems
Solving Classification Problems
Random Variables
Bayes Theorem
Naive Bayes Classifier
Naive Bayes Classifier: An example
K-Nearest Neighbours
K-Nearest Neighbours: A few wrinkles
Support Vector Machines Introduced
Support Vector Machines: Maximum Margin Hyperplane and Kernel Trick
Artificial Neural Networks: Perceptrons Introduced
Clustering as a form of Unsupervised learning
Clustering: Introduction
Clustering: K-Means and DBSCAN
Association Detection
Association Rules Learning
Dimensionality Reduction
Dimensionality Reduction
Principal Component Analysis
Regression as a form of supervised learning
Regression Introduced: Linear and Logistic Regression
Bias Variance Trade-off
Natural Language Processing and Python
Applying ML to Natural Language Processing
Installing Python - Anaconda and Pip
Natural Language Processing with NLTK
Natural Language Processing with NLTK - See it in action
Web Scraping with BeautifulSoup
A Serious NLP Application: Text Auto Summarization using Python
Python Drill: Autosummarize News Articles I
Python Drill: Autosummarize News Articles II
Python Drill: Autosummarize News Articles III
Put it to work: News Article Classification using K-Nearest Neighbors
Put it to work : News Article Classification using Naive Bayes Classifier
Python Drill: Scraping News Websites
Python Drill: Feature Extraction with NLTK
Python Drill: Classification with KNN
Python Drill: Classification with Naive Bayes
Document Distance using TF-IDF
Put it to work: News Article Clustering with K-Means and TF-IDF
Python Drill: Clustering with K Means
Sentiment Analysis
Solve Sentiment Analysis using Machine Learning
Sentiment Analysis - What's all the fuss about?
ML Solutions for Sentiment Analysis - the devil is in the details
Sentiment Lexicons (with an introduction to WordNet and SentiWordNet)
Regular Expressions
Regular Expressions in Python
Put it to work: Twitter Sentiment Analysis
Twitter Sentiment Analysis - Work the API
Twitter Sentiment Analysis - Regular Expressions for Preprocessing
Twitter Sentiment Analysis - Naive Bayes, SVM and Sentiwordnet
Decision Trees
Using Tree Based Models for Classification
Planting the seed - What are Decision Trees?
Growing the Tree - Decision Tree Learning
Branching out - Information Gain
Decision Tree Algorithms
Titanic: Decision Trees predict Survival (Kaggle) – I
Titanic: Decision Trees predict Survival (Kaggle) - II
Titanic: Decision Trees predict Survival (Kaggle) – III
A Few Useful Things to Know About Overfitting
Overfitting - the bane of Machine Learning
Overfitting Continued
Cross Validation
Simplicity is a virtue – Regularization
The Wisdom of Crowds - Ensemble Learning
Ensemble Learning continued - Bagging, Boosting and Stacking
Random Forests
Random Forests - Much more than trees
Back on the Titanic - Cross Validation and Random Forests
Recommendation Systems
Solving Recommendation Problems
What do Amazon and Netflix have in common?
Recommendation Engines - A look inside
What are you made of? - Content-Based Filtering
With a little help from friends - Collaborative Filtering
A Neighbourhood Model for Collaborative Filtering
Top Picks for You! - Recommendations with Neighbourhood Models
Discover the Underlying Truth - Latent Factor Collaborative Filtering
Latent Factor Collaborative Filtering contd.
Gray Sheep and Shillings - Challenges with Collaborative Filtering
The Apriori Algorithm for Association Rules
Recommendation Systems in Python
Back to Basics: Numpy in Python
Back to Basics: Numpy and Scipy in Python
Movielens and Pandas
Code Along - What's my favourite movie? - Data Analysis with Pandas
Code Along - Movie Recommendation with Nearest Neighbour CF
Code Along - Top Movie Picks (Nearest Neighbour CF)
Code Along - Movie Recommendations with Matrix Factorization
Code Along - Association Rules with the Apriori Algorithm
A Taste of Deep Learning and Computer Vision
Computer Vision - An Introduction
Perceptron Revisited
Deep Learning Networks Introduced
Code Along - Handwritten Digit Recognition -I
Code Along - Handwritten Digit Recognition - II
Code Along - Handwritten Digit Recognition – III

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

Authors

Table of Contents

Introduction
You, This Course and Us
A sneak peek at what's coming up
Jump right in: Machine learning for Spam detection
Solving problems with computers
Machine Learning: Why should you jump on the bandwagon?
Plunging In - Machine Learning Approaches to Spam Detection
Spam Detection with Machine Learning Continued
Get the Lay of the Land: Types of Machine Learning Problems
Solving Classification Problems
Solving Classification Problems
Random Variables
Bayes Theorem
Naive Bayes Classifier
Naive Bayes Classifier: An example
K-Nearest Neighbours
K-Nearest Neighbours: A few wrinkles
Support Vector Machines Introduced
Support Vector Machines: Maximum Margin Hyperplane and Kernel Trick
Artificial Neural Networks: Perceptrons Introduced
Clustering as a form of Unsupervised learning
Clustering: Introduction
Clustering: K-Means and DBSCAN
Association Detection
Association Rules Learning
Dimensionality Reduction
Dimensionality Reduction
Principal Component Analysis
Regression as a form of supervised learning
Regression Introduced: Linear and Logistic Regression
Bias Variance Trade-off
Natural Language Processing and Python
Applying ML to Natural Language Processing
Installing Python - Anaconda and Pip
Natural Language Processing with NLTK
Natural Language Processing with NLTK - See it in action
Web Scraping with BeautifulSoup
A Serious NLP Application: Text Auto Summarization using Python
Python Drill: Autosummarize News Articles I
Python Drill: Autosummarize News Articles II
Python Drill: Autosummarize News Articles III
Put it to work: News Article Classification using K-Nearest Neighbors
Put it to work : News Article Classification using Naive Bayes Classifier
Python Drill: Scraping News Websites
Python Drill: Feature Extraction with NLTK
Python Drill: Classification with KNN
Python Drill: Classification with Naive Bayes
Document Distance using TF-IDF
Put it to work: News Article Clustering with K-Means and TF-IDF
Python Drill: Clustering with K Means
Sentiment Analysis
Solve Sentiment Analysis using Machine Learning
Sentiment Analysis - What's all the fuss about?
ML Solutions for Sentiment Analysis - the devil is in the details
Sentiment Lexicons (with an introduction to WordNet and SentiWordNet)
Regular Expressions
Regular Expressions in Python
Put it to work: Twitter Sentiment Analysis
Twitter Sentiment Analysis - Work the API
Twitter Sentiment Analysis - Regular Expressions for Preprocessing
Twitter Sentiment Analysis - Naive Bayes, SVM and Sentiwordnet
Decision Trees
Using Tree Based Models for Classification
Planting the seed - What are Decision Trees?
Growing the Tree - Decision Tree Learning
Branching out - Information Gain
Decision Tree Algorithms
Titanic: Decision Trees predict Survival (Kaggle) – I
Titanic: Decision Trees predict Survival (Kaggle) - II
Titanic: Decision Trees predict Survival (Kaggle) – III
A Few Useful Things to Know About Overfitting
Overfitting - the bane of Machine Learning
Overfitting Continued
Cross Validation
Simplicity is a virtue – Regularization
The Wisdom of Crowds - Ensemble Learning
Ensemble Learning continued - Bagging, Boosting and Stacking
Random Forests
Random Forests - Much more than trees
Back on the Titanic - Cross Validation and Random Forests
Recommendation Systems
Solving Recommendation Problems
What do Amazon and Netflix have in common?
Recommendation Engines - A look inside
What are you made of? - Content-Based Filtering
With a little help from friends - Collaborative Filtering
A Neighbourhood Model for Collaborative Filtering
Top Picks for You! - Recommendations with Neighbourhood Models
Discover the Underlying Truth - Latent Factor Collaborative Filtering
Latent Factor Collaborative Filtering contd.
Gray Sheep and Shillings - Challenges with Collaborative Filtering
The Apriori Algorithm for Association Rules
Recommendation Systems in Python
Back to Basics: Numpy in Python
Back to Basics: Numpy and Scipy in Python
Movielens and Pandas
Code Along - What's my favourite movie? - Data Analysis with Pandas
Code Along - Movie Recommendation with Nearest Neighbour CF
Code Along - Top Movie Picks (Nearest Neighbour CF)
Code Along - Movie Recommendations with Matrix Factorization
Code Along - Association Rules with the Apriori Algorithm
A Taste of Deep Learning and Computer Vision
Computer Vision - An Introduction
Perceptron Revisited
Deep Learning Networks Introduced
Code Along - Handwritten Digit Recognition -I
Code Along - Handwritten Digit Recognition - II
Code Along - Handwritten Digit Recognition – III

Video Details

ISBN 139781788624329
Course Length19 hours 15 minutes
Read More

Read More Reviews

Recommended for You

From 0 to 1: Data Structures & Algorithms in Java [Video] Book Cover
From 0 to 1: Data Structures & Algorithms in Java [Video]
$ 32.99
$ 28.05
The Complete jQuery Course: From Beginner To Advanced! [Video] Book Cover
The Complete jQuery Course: From Beginner To Advanced! [Video]
$ 191.99
$ 163.20
The Complete Sass & SCSS Course: From Beginner to Advanced [Video] Book Cover
The Complete Sass & SCSS Course: From Beginner to Advanced [Video]
$ 191.99
$ 163.20
Learn To Program Tic-Tac-Toe with C# and Visual Studio [Video] Book Cover
Learn To Program Tic-Tac-Toe with C# and Visual Studio [Video]
$ 42.99
$ 36.55
From 0 to 1 : Spark for Data Science with Python [Video] Book Cover
From 0 to 1 : Spark for Data Science with Python [Video]
$ 32.99
$ 28.05
Complete MATLAB Tutorial: Go from Beginner to Pro [Video] Book Cover
Complete MATLAB Tutorial: Go from Beginner to Pro [Video]
$ 196.99
$ 167.45