Displaying 1-5 of 5 result(s).
- What this course is about
- Section 2: Jump right in : Machine learning for Spam detection
- 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
- Section 3: Naive Bayes Classifier
- Random Variables
- Bayes Theorem
- Naive Bayes Classifier
- Naive Bayes Classifier : An example
- Section 4: K-Nearest Neighbors
- K-Nearest Neighbors
- K-Nearest Neighbors : A few wrinkles
- Section 5: Support Vector Machines
- Support Vector Machines Introduced
- Support Vector Machines : Maximum Margin Hyperplane and Kernel Trick
- Section 6: Clustering as a form of Unsupervised learning
- Clustering : Introduction
- Clustering : K-Means and DBSCAN
- Section 7: Association Detection
- Association Rules Learning
- Section 8: Dimensionality Reduction
- Dimensionality Reduction
- Section 9: Principal Component Analysis
- Artificial Neural Networks
- Artificial Neural Networks:Perceptrons Introduced
- Section 10: Regression as a form of supervised learning
- Regression Introduced : Linear and Logistic Regression
- Bias Variance Trade-off
- Section 11: Natural Language Processing and Python
- 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
- Section 12: Sentiment Analysis
- A Sneak Peek at what's coming up
- 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
- Not for you? No problem.
30 day money back guarantee.
- Forever yours.
- Learn on the go.
Desktop, iOS and Android.
- Get rewarded.
Certificate of completion.
- 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?
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
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