Introduction to Machine Learning

🎓 Mastering Machine Learning with Python: A Deep Dive into Linear & Logistic Regression and Neural Networks

Course Outcome:

Explore the exciting world of machine learning algorithms with our comprehensive course, designed to equip you with the skills to implement Python-based regression and classification techniques for real-world datasets. By completing this course, you will be proficient in:

• Understanding machine learning concepts and applications 🚀
• Implementing Linear Regression models for regression tasks
• Mastering Logistic Regression for binary classification problems
• Applying Neural Networks to solve multi-class classification challenges

Course Topics and Approach:

Dive into the fascinating realm of Supervised Learning with our introductory course on machine learning. This course is tailored to cover:

• Core Algorithms: A deep dive into the math behind Linear Regression, Logistic Regression, and Neural Networks, including optimization algorithms and back propagation formulas 📊
• Python Implementation: Comprehensive explanations on converting these algorithms into Python code with an emphasis on design, vectorization techniques, and hands-on practice.
• Real-World Applications: Practical case studies using real-world datasets to classify images, detect spam in text messages, and predict house prices.

Course Audience:

This course is the perfect fit for:

• Professionals: Scientists, engineers, programmers, and anyone passionate about machine learning or data science looking to expand their skillset.
• No Experience Required: Absolute beginners in machine learning are also welcome!

Prerequisites:

• Basic understanding of linear algebra (vectors, matrix multiplication, transpose)
• Knowledge of multivariable calculus (to grasp optimization and backpropagation formulas)
• Proficiency in Python 3 programming

Students should have a Python environment, like Anaconda, set up on their machine to run commands and Jupyter Notebooks.

Teaching Style and Resources:

Our course is designed with engaging content and hands-on exercises to ensure an enriching learning experience:

• Visual Learning: Benefit from a plethora of examples accompanied by plots, enhancing your understanding of the material.
• Extensive Practice: With over 50+ exercises complete with solutions, you'll gain invaluable practice through theoretical work, Jupyter Notebooks, and programming challenges.
• Comprehensive Resources: All course materials, including presentations, supplementary documents, demonstrations, code snippets, and solutions to the exercises, are available on our dedicated Github repository.

What You Will Learn:

📈 Linear Regression: Understand how to model predictive relationships between variables using this fundamental algorithm.

👨‍⚖️ Logistic Regression: Discover the method behind binary classification problems and learn to interpret probabilities effectively.

🤖 Neural Networks: Explore the basics of neural networks, including structure, training, and applications in both binary and multi-class classification tasks.

Join Us on This Journey!

Embark on a transformative journey into the heart of machine learning with Python as your guide. Whether you're looking to advance your career or simply satisfy your curiosity about this cutting-edge field, our course will equip you with the practical skills and theoretical understanding necessary to succeed. Sign up now and let's embark on this exciting adventure together! 🌟