Coursera

Machine Learning Specialization

Hybrid

2 months

English

Generative AI

Course Overview

The Machine Learning Specialization from Stanford University provides a comprehensive learning experience for those looking to master machine learning (ML) and deep learning (DL) algorithms. In this advanced course, you will dive deep into both supervised and unsupervised learning techniques, building real-world applications using industry-leading tools like NumPy, scikit-learn, and TensorFlow.

The course covers the essential building blocks of machine learning and deep learning, starting with supervised learning models for prediction and binary classification tasks using techniques such as linear regression and logistic regression. You will also work with neural networks in TensorFlow, learning how to design and train deep learning models for multi-class classification.

The course delves into decision trees, tree ensemble methods, and best practices for ML development, giving you a well-rounded skill set. You will also explore unsupervised learning, including clustering and anomaly detection, and apply these techniques to real-world datasets.

What You'll Learn

- Master ML Algorithms: Learn how to build and train ML models for supervised tasks such as regression, classification, and more.
- Deep Learning Expertise: Build neural networks with TensorFlow and learn multi-class classification and reinforcement learning techniques.
- Hands-On Projects: Work on practical applications such as building recommender systems, unsupervised learning models, and deep reinforcement learning.
- Practical ML Skills: Gain in-depth knowledge of clustering, anomaly detection, decision trees, and ensemble methods in machine learning.
- Career Certificate: Earn a certificate from Stanford University that will demonstrate your expertise to employers and advance your career.