Data Science - Supervised Machine Learning in Python

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Overview

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This course covers the core concepts of supervised machine learning using Python. You will learn the most popular algorithms, such as K-Nearest Neighbor (KNN), Naive Bayes, and Decision Trees, and how to implement them with Python on real-world datasets. By the end of the course, you'll be ready to build and deploy machine learning models. This comprehensive course will guide you through the core techniques of supervised machine learning using Python. You will begin with an introduction to essential concepts and then dive into hands-on coding exercises, where you’ll implement key algorithms such as K-Nearest Neighbor (KNN), Naive Bayes, and Decision Trees. Working with real datasets like MNIST, you'll gain practical experience in solving problems and understanding how these algorithms perform in the real world. As you progress, you'll develop practical skills in hyperparameter tuning, cross-validation, and feature extraction, which are critical for optimizing machine learning models. You'll explore advanced topics like Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and non-Naive Bayes methods, adding depth to your knowledge of more complex machine learning techniques. The course culminates in learning how to deploy machine learning models as web services, providing you with a complete understanding of machine learning application in real-world. Targeted at aspiring data scientists and machine learning engineers, this course combines theory and practical exercises to ensure you build strong, deployable machine learning models. While no prior machine learning experience is required, familiarity with Python programming is recommended. This course is ideal for aspiring data scientists and machine learning engineers who wish to deepen their understanding of supervised machine learning algorithms. It is also perfect for developers looking to transition into the field of data science and those familiar with Python programming who want to enhance their machine learning skills. This course adopts a project-based learning approach. You will go through the theory behind machine learning algorithms and then immediately apply that theory to real-world datasets. Each section of the course contains coding examples and exercises that help solidify your understanding. This course is based on Data Science - Supervised Machine Learning in Python, by The Lazy Programmer. This video is licensed and distributed by Packt. All rights reserved. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.

Syllabus

Introduction and Review
- This module provides an overview of the course structure, essential tips for success, and a review of foundational concepts necessary for the rest of the course. Learners will gain clarity on course expectations and resource locations while reinforcing key prior knowledge.
K-Nearest Neighbor
- This module explores the K-Nearest Neighbor (KNN) algorithm, covering its intuition, core concepts, code implementation, and real-world challenges. Learners will understand how KNN works, when it fails, and how to tune it effectively for better performance. The module also includes hands-on practice with Python and the MNIST dataset.
Naive Bayes and Bayes Classifiers
- This module covers the fundamentals of Bayesian classifiers, including their application to both continuous and discrete data, the Naive Bayes algorithm, and advanced techniques like Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA). Learners will gain hands-on experience implementing these models in Python and understand the differences between generative and discriminative approaches.
Decision Trees
- This module explores the fundamentals of decision trees, including their structure, how they make splits, and how to implement them in code. Learners will gain an understanding of entropy, information gain, and practical applications in classification and regression tasks.
Perceptrons
- This module introduces the perceptron, a foundational element of neural networks, and explores its implementation, applications, and limitations. Learners will gain hands-on experience coding a perceptron and applying it to real-world problems like digit classification and logical operations. The module also covers the role of loss functions in training and optimization.
Practical Machine Learning
- This module covers essential techniques for building and optimizing machine learning models, including hyperparameter tuning, feature selection, and the use of the Sci-Kit Learn library. Learners will gain hands-on experience with regression, classification, and comparison with deep learning approaches. The module emphasizes practical implementation and model evaluation strategies.
Building a Machine Learning Web Service
- This module explores the essential steps for deploying a machine learning model as a web service. Learners will gain hands-on experience with Python-based implementation, API design, and integration strategies. The material covers both conceptual foundations and practical coding techniques.
Conclusion
- This module explores advanced machine learning techniques, focusing on support vector machines and ensemble methods like random forest. Learners will gain an understanding of how these models work and how they can be applied in real-world scenarios.