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From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase
Quick Facts
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course and certificate fees
Fees information
₹ 599 ₹3,099
certificate availability
Yes
certificate providing authority
Udemy
The syllabus
Introduction
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 Neighbors
- K-Nearest Neighbors : 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 favorite 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 - II
Quizzes
- Machine Learning Jump Right In
- Machine Learning Jump Right In -II
- Machine Learning Algorithms
- Types of ML problems
- Random Variables
- Bayes theorem
- Naive Bayes
- Naive Bayes
- Classification
- Naive Bayes
- kNN Algorithm
- kNN Algorithm
- SVM
- SVM
- Clustering
- Association rule learning
- Dimensionality Reduction
- PCA
- Artificial Neural Network
- Artificial Neural Network
- Regression
- Bias Variance Tradeoff
- NLP
- NLP Bayes
- NLP kNN
- TF-IDF
- NLP k-means
Instructors
Articles
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