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    • From 0 to 1 Machine Learning Natural Language Processing and Python Cut to the Chase

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

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    Udemy

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    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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