Machine Learning Fundamentals with Python

This course is ideal for beginners and professionals who want to learn machine learning with Python in a practical way. Whether you are starting fresh or looking to strengthen your skills, youll gain a solid foundation through concepts, examples, and hands-on labs.

Artificial intelligence (AI) is the broader concept of machines being able to perform tasks that typically require human intelligence. Machine learning (ML) is a subset of AI that focuses on creating algorithms that can learn from data and improve over time. This Python machine learning course helps you understand ML fundamentals within the wider AI landscape.

The course covers the essentials of machine learning: regression, classification, clustering, dimensionality reduction, recommender systems, and evaluation metrics. Youll also explore the link between statistical modeling and machine learning through hands-on Python labs.

Yes. A basic understanding of Python, along with knowledge of data analysis, visualization techniques, and high school-level mathematics, is recommended before enrolling in the machine learning fundamentals course.

Supervised learning uses labeled data to train algorithms for regression and classification tasks, while unsupervised learning works with unlabeled data to find hidden patterns through clustering or dimensionality reduction. Both are explained in detail in this Machine Learning Fundamentals with Python course.

Youll learn core algorithms including linear regression, logistic regression, decision trees, support vector machines, K-nearest neighbors, K-means clustering, hierarchical clustering, random forests, and recommender systems with Python.

Through practical labs and a final project, youll gain experience applying algorithms to datasets, evaluating models using metrics like RMSE and accuracy, and building recommender systems. This prepares you to solve real-world problems using machine learning.

Yes. Each module includes labs in Python where youll apply the concepts you learn, and the course concludes with a final project that brings everything together.

You should have basic Python skills, familiarity with data analysis and visualization, and comfort with high school-level math concepts.

The Machine Learning Fundamentals with Python course can typically be completed in a few weeks, depending on your pace and weekly time commitment.

Yes, you will earn an IBM Certificate that will validate your skills and can be shared with employers or on LinkedIn.

The course covers both in detail, from simple and multiple linear regression to logistic regression. Youll also learn about evaluation metrics and practical implementations using Python.

Yes, the syllabus includes recommender systems, covering both content-based and collaborative filtering techniques.

Absolutely. By completing this machine learning fundamentals Course, youll gain practical skills in regression, classification, clustering, and model evaluation that are directly applicable to data science roles.

The final project challenges you to build, evaluate, and refine machine learning models using Python, giving you practical experience you can showcase in your portfolio.

The IBM machine learning course highlights the differences and overlaps between statistical modeling and machine learning, helping you understand when to use each approach.