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

This course is very visual : most of the techniques are explained with the help of animations to help you Scholarly articles for machine learning approaches for natural language processing
  • 2399
    M.R.P 2999
    You Save:600 (20.01%)


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: Online Course
: Loonycorn
13 Sales 3.7 (64 vote)
Displaying 1-5 of 5 result(s).

Curriculum

    • Section 1: Introduction
    1. What this course is about
    • Section 2: Jump right in : Machine learning for Spam detection
    1. Machine Learning: Why should you jump on the bandwagon?
    2. Plunging In - Machine Learning Approaches to Spam Detection
    3. Spam Detection with Machine Learning Continued
    4. Get the Lay of the Land : Types of Machine Learning Problems
    • Section 3: Naive Bayes Classifier
    1. Random Variables
    2. Bayes Theorem
    3. Naive Bayes Classifier
    4. Naive Bayes Classifier : An example
    • Section 4: K-Nearest Neighbors
    1. K-Nearest Neighbors
    2. K-Nearest Neighbors : A few wrinkles
    • Section 5: Support Vector Machines
    1. Support Vector Machines Introduced
    2. Support Vector Machines : Maximum Margin Hyperplane and Kernel Trick
    • Section 6: Clustering as a form of Unsupervised learning
    1. Clustering : Introduction
    2. Clustering : K-Means and DBSCAN
    • Section 7: Association Detection
    1. Association Rules Learning
    • Section 8: Dimensionality Reduction
    1. Dimensionality Reduction
    • Section 9: Principal Component Analysis
    1. Artificial Neural Networks
    2. Artificial Neural Networks:Perceptrons Introduced
    • Section 10: Regression as a form of supervised learning
    1. Regression Introduced : Linear and Logistic Regression
    2. Bias Variance Trade-off
    • Section 11: Natural Language Processing and Python
    1. Installing Python - Anaconda and Pip
    2. Natural Language Processing with NLTK
    3. Natural Language Processing with NLTK - See it in action
    4. Web Scraping with BeautifulSoup
    5. A Serious NLP Application : Text Auto Summarization using Python
    6. Python Drill : Autosummarize News Articles I
    7. Python Drill : Autosummarize News Articles II
    8. Python Drill : Autosummarize News Articles III
    9. Put it to work : News Article Classification using K-Nearest Neighbors
    10. Put it to work : News Article Classification using Naive Bayes Classifier
    11. Python Drill : Scraping News Websites
    12. Python Drill : Feature Extraction with NLTK
    13. Python Drill : Classification with KNN
    14. Python Drill : Classification with Naive Bayes
    15. Document Distance using TF-IDF
    16. Put it to work : News Article Clustering with K-Means and TF-IDF
    17. Python Drill : Clustering with K Means
    • Section 12: Sentiment Analysis
    1. A Sneak Peek at what's coming up
    2. Sentiment Analysis - What's all the fuss about?
    3. ML Solutions for Sentiment Analysis - the devil is in the details
    4. Sentiment Lexicons ( with an introduction to WordNet and SentiWordNet)
    5. Regular Expressions
    6. Regular Expressions in Python

Key Features

    • Not for you? No problem.
      30 day money back guarantee.
    • Forever yours.
      Lifetime access.
    • Learn on the go.
      Desktop, iOS and Android.
    • Get rewarded.
      Certificate of completion.

About Course

    • Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.
    • In machine learning course, students can learn deep learning of Neural network python.
    • 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.
    • Students can learn NLTK python which is very useful for Beginners

What is covered in this Course?

  • Machine Learning:

    Supervised/Unsupervised learning, Classification, Clustering, Association Detection, Anomaly Detection, Dimensionality Reduction, Regression.

    Naive Bayes, K-nearest neighbours, Support Vector Machines, Artificial Neural Networks, K-means, Hierarchical clustering, Principal Components Analysis, Linear regression, Logistics regression, Random variables, Bayes theorem, Bias-variance tradeoff

    Natural Language Processing with Python:

    Corpora, stopwords, sentence and word parsing, auto-summarization, sentiment analysis (as a special case of classification), TF-IDF, Document Distance, Text summarization, Text classification with Naive Bayes and K-Nearest Neighbours and Clustering with K-Means

    Sentiment Analysis:

    Why it's useful, Approaches to solving - Rule-Based , ML-Based , Training , Feature Extraction, Sentiment Lexicons, Regular Expressions, Twitter API, Sentiment Analysis of Tweets with Python

    A Note on Python:

    The code-alongs in this class all use Python 2.7. Source code (with copious amounts of comments) is attached as a resource with all the code-alongs. The source code has been provided for both Python 2 and Python 3 wherever possible.

    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

Who should buy this course?

    • No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.
    • Analytics professionals, modelers, big data professionals who haven't had exposure to machine learning
    • Engineers who want to understand or learn machine learning and apply it to problems they are solving
    • Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
    • Tech executives and investors who are interested in big data, machine learning or natural language processing
    • MBA graduates or business professionals who are looking to move to a heavily quantitative role
 
Discussion
Kalpesh Patel

5 months ago

I like animated course.. really very useful course

KreemaJoshi

5 months ago

Techniques are explained very well. I like it.

RichaPatel

5 months ago

Nice course.

Hemant Patel

5 months ago

Useful course. I hv find a good course after a long time.

Nishant Patel

5 months ago

it's helpful to intelligent conversations with data scientists and engineers about machine learning for me.

Dharsh Patel

5 months ago

good practical experience.

NayanShukla

5 months ago

in this course most helpful thing I have found, is repetition, zooming out so easy to understand. sound quality is also nice. I love this course.

Nax Patel

5 months ago

course does not seem very helpful.

Savant Joshi

5 months ago

Good course it is.

Feedback
3.7
6 Ratings
user1
4 months ago

The team provide an amazing learning experience and are also responsive to doubts that are raised on the discussion forums.

user2
4 months ago

Great stuff, Explanations are clear.

user3
4 months ago

The way of presentations, explanations are very good. . Perfect package for a beginner.

user4
4 months ago

Covers all basics of Machine learning concepts with easy methodology and good enough practical examples.

user5
4 months ago

Excellent efforts by loonycorn team. Easy to understand.

user6
4 months ago

Avarage Course. But clear and detailed explanation of image classification

 
Provided by
L

Loonycorn is us, Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh. Between the four of us, we have studied at Stanford, IIM Ahmedabad, the IITs and have spent years (decades, actually) working in tech, in the Bay Area, New York, Singapore and Bangalore.

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