Curriculum
15 Sections
92 Lessons
52 Weeks
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Introduction
1
2.1
A sneak peek at what’s coming up
4 Minutes
Jump right in : Machine learning for Spam detection
5
3.1
Solving problems with computers
2 Minutes
3.1
Machine Learning: Why should you jump on the bandwagon?
7 Minutes
3.1
Plunging In – Machine Learning Approaches to Spam Detection
12 Minutes
3.1
Spam Detection with Machine Learning Continued
11 Minutes
3.1
Get the Lay of the Land : Types of Machine Learning Problems
10 Minutes
Solving Classification Problems
10
5.1
Solving Classification Problems
1 Minute
5.2
Random Variables
11 Minutes
5.3
Bayes Theorem
12 Minutes
5.4
Naive Bayes Classifier
5 Minutes
5.5
Naive Bayes Classifier : An example
9 Minutes
5.6
K-Nearest Neighbors
13 Minutes
5.7
K-Nearest Neighbors : A few wrinkles
14 Minutes
5.8
Support Vector Machines Introduced
8 Minutes
5.9
Support Vector Machines : Maximum Margin Hyperplane and Kernel Trick
16 Minutes
5.10
Artificial Neural Networks:Perceptrons Introduced
11 Minutes
Clustering as a form of Unsupervised learning
2
6.1
Clustering : Introduction
31 Minutes
6.1
Clustering : K-Means and DBSCAN
14 Minutes
Association Detection
1
7.1
Association Rules Learning
9 Minutes
Dimensionality Reduction
2
8.1
Dimensionality Reduction
10 Minutes
8.1
Principal Component Analysis
19 Minutes
Regression as a form of supervised learning
2
9.1
Regression Introduced : Linear and Logistic Regression
14 Minutes
9.1
Bias Variance Trade-off
10 Minutes
Natural Language Processing and Python
18
10.1
Applying ML to Natural Language Processing
1 Minute
10.1
Put it to work : News Article Classification using Naive Bayes Classifier
19 Minutes
10.1
Put it to work : News Article Clustering with K-Means and TF-IDF
15 Minutes
10.1
Document Distance using TF-IDF
11 Minutes
10.1
Python Drill : Classification with Naive Bayes
8 Minutes
10.1
Python Drill : Classification with KNN
4 Minutes
10.1
Python Drill : Feature Extraction with NLTK
19 Minutes
10.1
Python Drill : Scraping News Websites
16 Minutes
10.1
Put it to work : News Article Classification using K-Nearest Neighbors
20 Minutes
10.1
Installing Python – Anaconda and Pip
9 Minutes
10.1
Python Drill : Autosummarize News Articles III
10 Minutes
10.1
Python Drill : Autosummarize News Articles II
11 Minutes
10.1
Python Drill : Autosummarize News Articles I
18 Minutes
10.1
A Serious NLP Application : Text Auto Summarization using Python
11 Minutes
10.1
Web Scraping with BeautifulSoup
18 Minutes
10.1
Natural Language Processing with NLTK – See it in action
14 Minutes
10.1
Natural Language Processing with NLTK
7 Minutes
10.1
Python Drill : Clustering with K Means
8 Minutes
Sentiment Analysis
10
11.1
Solve Sentiment Analysis using Machine Learning
3 Minutes
11.1
Sentiment Analysis – What’s all the fuss about?
17 Minutes
11.1
ML Solutions for Sentiment Analysis – the devil is in the details
20 Minutes
11.1
Sentiment Lexicons ( with an introduction to WordNet and SentiWordNet)
18 Minutes
11.1
Regular Expressions
18 Minutes
11.1
Regular Expressions in Python
6 Minutes
11.1
Put it to work : Twitter Sentiment Analysis
18 Minutes
11.1
Twitter Sentiment Analysis – Work the API
28 Minutes
11.1
Twitter Sentiment Analysis – Regular Expressions for Preprocessing
13 Minutes
11.1
Twitter Sentiment Analysis – Naive Bayes, SVM and Sentiwordnet
20 Minutes
Decision Trees
8
12.1
Using Tree Based Models for Classification
1 Minute
12.1
Planting the seed – What are Decision Trees?
17 Minutes
12.1
Growing the Tree – Decision Tree Learning
18 Minutes
12.1
Branching out – Information Gain
18 Minutes
12.1
Decision Tree Algorithms
7 Minutes
12.1
Titanic : Decision Trees predict Survival (Kaggle) – I
19 Minutes
12.1
Titanic : Decision Trees predict Survival (Kaggle) – II
14 Minutes
12.1
Titanic : Decision Trees predict Survival (Kaggle) – III
13 Minutes
A Few Useful Things to Know About Over-fitting
6
13.1
Overfitting – the bane of Machine Learning
19 Minutes
13.1
Overfitting Continued
11 Minutes
13.1
Cross Validation
18 Minutes
13.1
Simplicity is a virtue – Regularization
7 Minutes
13.1
The Wisdom of Crowds – Ensemble Learning
17 Minutes
13.1
Ensemble Learning continued – Bagging, Boosting and Stacking
18 Minutes
Random Forests
2
14.1
Random Forests – Much more than trees
12 Minutes
14.1
Back on the Titanic – Cross Validation and Random Forests
20 Minutes
Recommendation Systems
11
16.1
Solving Recommendation Problems
1 Minute
16.1
What do Amazon and Netflix have in common?
16 Minutes
16.1
Recommendation Engines – A look inside
10 Minutes
16.1
What are you made of? – Content-Based Filtering
13 Minutes
16.1
With a little help from friends – Collaborative Filtering
10 Minutes
16.1
A Neighbourhood Model for Collaborative Filtering
18 Minutes
16.1
Top Picks for You! – Recommendations with Neighbourhood Models
10 Minutes
16.1
Discover the Underlying Truth – Latent Factor Collaborative Filtering
20 Minutes
16.1
Latent Factor Collaborative Filtering contd.
12 Minutes
16.1
Gray Sheep and Shillings – Challenges with Collaborative Filtering
8 Minutes
16.1
The Apriori Algorithm for Association Rules
18 Minutes
Recommendation Systems in Python
8
17.1
Back to Basics : Numpy in Python
18 Minutes
17.1
Back to Basics : Numpy and Scipy in Python
14 Minutes
17.1
Movielens and Pandas
16 Minutes
17.1
What’s my favorite movie? – Data Analysis with Pandas
6 Minutes
17.1
Movie Recommendation with Nearest Neighbour CF
18 Minutes
17.1
Top Movie Picks (Nearest Neighbour CF)
6 Minutes
17.1
Movie Recommendations with Matrix Factorization
18 Minutes
17.1
Association Rules with the Apriori Algorithm
10 Minutes
A Taste of Deep Learning and Computer Vision
6
18.1
Computer Vision – An Introduction
18 Minutes
18.1
Perceptron Revisited
16 Minutes
18.1
Deep Learning Networks Introduced
17 Minutes
18.1
Handwritten Digit Recognition -I
14 Minutes
18.1
Handwritten Digit Recognition – II
17 Minutes
18.1
Handwritten Digit Recognition – III
6 Minutes
From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase
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