English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 19h 15m | 7.11 GB
A down-to-earth, shy but confident take on machine learning techniques that you can put to work today
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 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
Table of Contents
01 You, This Course and Us
02 A sneak peek at what’s coming up
03 Solving problems with computers
04 Machine Learning – Why should you jump on the bandwagon
05 Plunging In – Machine Learning Approaches to Spam Detection
06 Spam Detection with Machine Learning Continued
07 Get the Lay of the Land – Types of Machine Learning Problems
08 Solving Classification Problems
09 Random Variables
10 Bayes Theorem
11 Naive Bayes Classifier
12 Naive Bayes Classifier – An example
13 K-Nearest Neighbours
14 K-Nearest Neighbours – A few wrinkles
15 Support Vector Machines Introduced
16 Support Vector Machines – Maximum Margin Hyperplane and Kernel Trick
17 Artificial Neural Networks – Perceptrons Introduced
18 Clustering – Introduction
19 Clustering – K-Means and DBSCAN
20 Association Rules Learning
21 Dimensionality Reduction
22 Principal Component Analysis
23 Regression Introduced – Linear and Logistic Regression
24 Bias Variance Trade-off
25 Applying ML to Natural Language Processing
26 Installing Python – Anaconda and Pip
27 Natural Language Processing with NLTK
28 Natural Language Processing with NLTK – See it in action
29 Web Scraping with BeautifulSoup
30 A Serious NLP Application – Text Auto Summarization using Python
31 Python Drill – Autosummarize News Articles I
32 Python Drill – Autosummarize News Articles II
33 Python Drill – Autosummarize News Articles III
34 Put it to work – News Article Classification using K-Nearest Neighbors
35 Put it to work – News Article Classification using Naive Bayes Classifier
36 Python Drill – Scraping News Websites
37 Python Drill – Feature Extraction with NLTK
38 Python Drill – Classification with KNN
39 Python Drill – Classification with Naive Bayes
40 Document Distance using TF-IDF
41 Put it to work – News Article Clustering with K-Means and TF-IDF
42 Python Drill – Clustering with K Means
43 Solve Sentiment Analysis using Machine Learning
44 Sentiment Analysis – What’s all the fuss about
45 ML Solutions for Sentiment Analysis – the devil is in the details
46 Sentiment Lexicons (with an introduction to WordNet and SentiWordNet)
47 Regular Expressions
48 Regular Expressions in Python
49 Put it to work – Twitter Sentiment Analysis
50 Twitter Sentiment Analysis – Work the API
51 Twitter Sentiment Analysis – Regular Expressions for Preprocessing
52 Twitter Sentiment Analysis – Naive Bayes, SVM and Sentiwordnet
53 Using Tree Based Models for Classification
54 Planting the seed – What are Decision Trees
55 Growing the Tree – Decision Tree Learning
56 Branching out – Information Gain
57 Decision Tree Algorithms
58 Titanic – Decision Trees predict Survival (Kaggle) – I
59 Titanic – Decision Trees predict Survival (Kaggle) – II
60 Titanic – Decision Trees predict Survival (Kaggle) – III
61 Overfitting – the bane of Machine Learning
62 Overfitting Continued
63 Cross Validation
64 Simplicity is a virtue – Regularization
65 The Wisdom of Crowds – Ensemble Learning
66 Ensemble Learning continued – Bagging, Boosting and Stacking
67 Random Forests – Much more than trees
68 Back on the Titanic – Cross Validation and Random Forests
69 Solving Recommendation Problems
70 What do Amazon and Netflix have in common
71 Recommendation Engines – A look inside
72 What are you made of – Content-Based Filtering
73 With a little help from friends – Collaborative Filtering
74 A Neighbourhood Model for Collaborative Filtering
75 Top Picks for You! – Recommendations with Neighbourhood Models
76 Discover the Underlying Truth – Latent Factor Collaborative Filtering
77 Latent Factor Collaborative Filtering contd.mp4
78 Gray Sheep and Shillings – Challenges with Collaborative Filtering
79 The Apriori Algorithm for Association Rules
80 Back to Basics – Numpy in Python
81 Back to Basics – Numpy and Scipy in Python
82 Movielens and Pandas
83 Code Along – What’s my favourite movie – Data Analysis with Pandas
84 Code Along – Movie Recommendation with Nearest Neighbour CF
85 Code Along – Top Movie Picks (Nearest Neighbour CF)
86 Code Along – Movie Recommendations with Matrix Factorization
87 Code Along – Association Rules with the Apriori Algorithm
88 Computer Vision – An Introduction
89 Perceptron Revisited
90 Deep Learning Networks Introduced
91 Code Along – Handwritten Digit Recognition -I
92 Code Along – Handwritten Digit Recognition – II
93 Code Along – Handwritten Digit Recognition – III
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