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

BY
Udemy

Mode

Online

Fees

₹ 599 3099

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study
Mode of Delivery Video and Text Based

Course and certificate fees

Fees information
₹ 599  ₹3,099
certificate availability

Yes

certificate providing authority

Udemy

The syllabus

Introduction

  • You, This Course and Us
  • Source Code and PDFs
  • 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 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

Mr Janani Ravi
Instructor
Udemy

Mr Vitthal Srinivasan
Instructor
Udemy

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