Machine Learning with Python: A Practical Introduction

An exciting and comprehensive course offered by IBM

Course Description

Welcome to "Machine Learning with Python: A Practical Introduction," an exciting and comprehensive course offered by IBM that will take you on a journey through the fascinating world of machine learning. This course is designed to provide you with a solid foundation in machine learning concepts and techniques using Python, one of the most popular and versatile programming languages in the data science field.

What students will learn

  • Understand the fundamental differences between supervised and unsupervised learning methods
  • Master supervised learning algorithms, including classification and regression techniques
  • Explore unsupervised learning algorithms, such as clustering and dimensionality reduction
  • Grasp the relationship between statistical modeling and machine learning
  • Analyze real-world applications of machine learning and its impact on society
  • Develop practical skills in building prediction models using classification techniques

Pre-requisites

While this course is designed for beginners, it is recommended that students have a basic understanding of Python programming. The course "Python Basics for Data Science" is suggested as a prerequisite to ensure you have the necessary foundation to excel in this machine learning course.

Course Coverage

  • Introduction to Machine Learning concepts and applications
  • Supervised Learning algorithms and techniques
  • Unsupervised Learning methods
  • Regression analysis (linear and non-linear)
  • Classification algorithms (K-Nearest Neighbor, Decision Trees, Logistic Regression, Support Vector Machines)
  • Clustering techniques (K-Means, Hierarchical, Density-Based)
  • Dimensionality Reduction
  • Model evaluation and performance metrics
  • Recommender Systems (Content-based and Collaborative Filtering)

Who this course is for

This course is perfect for aspiring data scientists, software engineers, analysts, or anyone interested in gaining practical knowledge of machine learning using Python. Whether you're a student looking to enhance your skillset or a professional aiming to transition into the field of data science, this course will provide you with the tools and knowledge needed to succeed.

Real-world Applications

The skills acquired in this course have numerous real-world applications across various industries. Students will be able to:

  • Develop predictive models for business forecasting and decision-making
  • Create recommendation systems for e-commerce platforms
  • Implement fraud detection algorithms in the finance sector
  • Analyze and classify medical data for improved healthcare outcomes
  • Optimize marketing strategies through customer segmentation
  • Enhance image and speech recognition systems

Syllabus

Module 1 - Introduction to Machine Learning

  • Applications of Machine Learning
  • Supervised vs Unsupervised Learning
  • Python libraries suitable for Machine Learning

Module 2 - Regression

  • Linear Regression
  • Non-linear Regression
  • Model evaluation methods

Module 3 - Classification

  • K-Nearest Neighbour
  • Decision Trees
  • Logistic Regression
  • Support Vector Machines
  • Model Evaluation

Module 4 - Unsupervised Learning

  • K-Means Clustering
  • Hierarchical Clustering
  • Density-Based Clustering

Module 5 - Recommender Systems

  • Content-based recommender systems
  • Collaborative Filtering

Conclusion

By enrolling in this course, you'll not only gain theoretical knowledge but also hands-on experience through practical labs. You'll have the opportunity to earn a valuable skill badge upon successful completion, showcasing your newly acquired expertise to potential employers. Don't miss this chance to dive into the world of machine learning and stay ahead in the rapidly evolving field of data science!