Machine Learning

IBM via edX Professional Certificate

Overview

Launch your career in machine learning (ML) with this intensive program designed to make you job-ready in less than three months. You'll gain in-demand skills in AI and machine learning while building a strong foundation in the theory, practice, and application of core algorithms and models.

Machine learning is a branch of artificial intelligence (AI) that enables computers to learn from data, adapt through algorithms and statistical models, and solve complex tasks traditionally requiring human intelligence. Proficiency in this field opens pathways to roles such as Machine Learning Engineer, NLP Scientist, and AI Engineer.

Throughout the program, you will combine comprehensive theory with hands-on practice across a wide range of topics. You will explore:

Supervised and Unsupervised learning
Regression and Classification
Clustering methods
Deep learning and Neural Networks
Reinforcement learning

Each concept is reinforced by coding exercises and applied projects that allow you to build practical experience with industry-relevant methods. The program culminates in a final capstone project, where you will integrate your skills into a portfolio-ready showcase that demonstrates your expertise to potential employers.

Career-Ready Outcomes

By the end of the program, you will have a portfolio of machine learning projects that highlight your technical abilities and applied knowledge. In addition, you will earn both a Professional Certificate and an IBM Digital Badge to validate your expertise. You will also gain access to IBM's career resources, which include resume-building support and mock interview practice to help you succeed in the job market.

Applied Learning Focus

A defining feature of this Professional Certificate is its emphasis on real-world applications. All courses include interactive labs and project-based learning that allow you to focus on areas of personal interest while developing professional skills. You will gain hands-on experience with widely used tools such as Jupyter Notebooks and Watson Studio, and you will work extensively with leading libraries, including Pandas, NumPy, Matplotlib, Seaborn, ipython-sql, Scikit-learn, SciPy, Keras, and TensorFlow. Along the way, you will deepen your understanding of algorithms ranging from regression, classification, and decision trees to ensemble methods, clustering approaches such as K-means and DBSCAN, dimensionality reduction, and survival analysis.

Through this balance of theory, practice, and applied projects, you will graduate with both the technical expertise and the real-world experience needed to excel in careers related to machine learning and deep learning.

Syllabus

Courses under this program:
Course 1: Machine Learning: Exploratory Data Analysis

Learn to retrieve, clean, and prepare data for using real-world tools and techniques. Gain essential skills for machine learning and AI that employers value.

Course 2: Machine Learning: Regression

This course covers key supervised machine learning topics in regression analysis. You’ll learn to train and test regression models. Ideal for aspiring data scientists and machine learning engineers.

Course 3: Machine Learning: Classification

This course covers key supervised machine learning (ML) and classification techniques, including logistic regression, decision trees, ensemble methods, and handling unbalanced datasets. Build and evaluate classification models using real-world data.

Course 4: Machine Learning: Unsupervised Models

This course introduces key unsupervised machine learning techniques and explains how to choose the best algorithm for your data. Learn clustering and dimensionality reduction to find insights in unlabeled data sets.

Course 5: Machine Learning: Deep & Reinforcement Learning

Get started in Reinforcement and Deep Learning! Explore the theory and practice behind neural networks, build deep learning models, and study modern architectures used in today’s AI applications.

Course 6: Machine Learning: Capstone Project

Demonstrate your machine learning expertise in this IBM Capstone course. Apply Pandas, Scikit-learn, TensorFlow/Keras, and build a real-world course recommender system. Showcase your ML skills through this comprehensive hands-on project.