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

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

Course Headline: A down-to-earth, shy but confident take on machine learning techniques that you can put to work today! 🚀

Prerequisites:

No need to be a math whiz or a Python pro! While some undergraduate level mathematics knowledge and familiarity with Python would be beneficial, they are not mandatory for getting started. Whether you're a beginner or looking to solidify your skills, this course is designed to help you at every step. 🛠️

Taught by Experts:

This course is crafted and delivered by a Stanford-educated ex-Googler and an IIT, IIM alumnus who was a lead analyst at Flipkart. Their combined decades of practical experience in quant trading, analytics, and e-commerce will provide you with insights that are both deep and real-world applicable. 🏫✨

Course Structure:

Simplified Learning: The course is designed to be down-to-earth, avoiding unnecessary complexities and focusing on what's essential for you to grasp the concepts and apply them immediately.

Practical & Confident Approach: We take a shy but confident stance, offering authoritative guidance without overcomplicating matters. Our aim is to empower you with knowledge that you can use today – no carburetor explanations here! 🚗➡️🚀

Visual Learning: Most techniques are explained with the help of animations to enhance your understanding and retention. Visual learning aids are a key component in making complex concepts clear and engaging. 🎨🧠

Hands-On Experience: This course is practical, providing you with hundreds of lines of well-commented source code in Python for implementing natural language processing (NLP) and machine learning techniques like text summarization and classification. You'll be able to apply what you learn directly to your own projects. 🧑‍💻

Quirky & Memorable: We believe in making learning fun and effective! Our course includes quirky examples, active learning with plenty of quizzes, a peppy soundtrack, and relevant artwork – all carefully chosen to improve your cognition and recall. 🎶🎉

What's Covered in the 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, etc.

Sentiment Analysis:

• Why it's crucial for understanding user sentiment
• Approaches to solving: Rule-Based, ML-Based
• Training models, Feature Extraction, Sentiment Lexicons, Regular Expressions, and even using the Twitter API for sentiments in tweets.

Mitigating Overfitting with Ensemble Learning:

• Understanding Decision Trees and decision tree learning
• Recognizing Overfitting and techniques to mitigate it (cross validation, regularization)
• Exploring Ensemble learning and Random forests for robust models

Recommendation Systems:

• Content based filtering
• Collaborative filtering
• Association Rules learning

Introduction to Deep Learning:

• Applying Multi-layer perceptrons to the MNIST Digit recognition problem using Python.

Note on Python:

All code-alongs in this class are based on Python 2.7, with source code provided for both Python 2 and Python 3 to ensure compatibility across different environments. 🐍➡️📚

Join us on this journey to master Machine Learning, NLP, and Python – where learning is fun, practical, and effective! 🎓✨