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

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.

Taught by a Stanford-educated ex-Googler and an IIT IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading analytics and e-commerce.

This course is a down-to-earth shy but confident take on machine learning techniques that you can put to work today

Let's parse that.

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.

Basic knowledge: 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.

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.

What's Covered:

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

Mitigating Overfitting with Ensemble Learning:

Decision trees and decision tree learning Overfitting in decision trees Techniques to mitigate overfitting (cross validation regularization) Ensemble learning and Random forests

Recommendations: Content based filtering Collaborative filtering and Association Rules learning

Get started with Deep learning: Apply Multi-layer perceptrons to the MNIST Digit recognition problem

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.

Who is the target audience?

• Yep! Analytics professionals modelers big data professionals who haven't had exposure to machine learning
• Yep! Engineers who want to understand or learn machine learning and apply it to problems they are solving
• Yep! Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
• Yep! Tech executives and investors who are interested in big data machine learning or natural language processing
• Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role

Course Completion Certificate

What you will learn:

• 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

Curriculum

Introduction:

• Jump right in: Machine learning for Spam detection
• Solving Classification Problems
• Clustering as a form of Unsupervised learning
• Association Detection
• Dimensionality Reduction
• Regression as a form of supervised learning
• Natural Language Processing and Python
• Sentiment Analysis
• Decision Trees
• A Few Useful Things to Know About Overfitting
• Random Forests
• Recommendation Systems
• Recommendation Systems in Python
• A Taste of Deep Learning and Computer Vision

Simpliv LLC, a platform for learning and teaching online courses. We basically focus on online learning which helps to learn business concepts, software technology to develop personal and professional goals through video library by recognized industry experts or trainers.

Why Simpliv

With the ever-evolving industry trends, there is a constant need of the professionally designed learning solutions that deliver key innovations on time and on a budget to achieve long-term success.

Simpliv understands the changing needs and allows the global learners to evaluate their technical abilities by aligning the learnings to key business objectives in order to fill the skills gaps that exist in the various business areas including IT, Marketing, Business Development, and much more.