The Machine Learning Specialization, offered by Stanford University and DeepLearning.AI, provides a beginner-friendly yet comprehensive program to master the fundamentals of machine learning. Led by Andrew Ng, this 3-course Specialization encompasses a broad introduction to modern machine learning, covering supervised learning, unsupervised learning, and best practices in artificial intelligence and machine learning innovation.
Throughout the program, you will build machine learning models using popular libraries such as NumPy, scikit-learn, and TensorFlow. The curriculum delves into advanced learning algorithms, including neural networks, decision trees, and tree ensemble methods, while also exploring unsupervised learning techniques such as clustering and anomaly detection. Additionally, you will learn to build recommender systems and a deep reinforcement learning model.
Upon completion, you will have gained practical know-how to apply machine learning to real-world challenges, making it an ideal starting point for those looking to break into AI or build a career in machine learning.
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Gain comprehensive knowledge and practical skills in supervised machine learning, advanced learning algorithms, and unsupervised learning, recommender systems, and reinforcement learning.
Explore the fundamentals of supervised machine learning, including building and training machine learning models in Python using popular libraries NumPy and scikit-learn. Gain practical experience in prediction and binary classification tasks, encompassing linear regression and logistic regression.
Delve into advanced learning algorithms, where you will build and train a neural network with TensorFlow to perform multi-class classification. Apply best practices for machine learning development to ensure your models generalize to real-world data and tasks. Additionally, learn to build and use decision trees and tree ensemble methods, including random forests and boosted trees.
Discover unsupervised learning techniques, including clustering and anomaly detection. Learn to build recommender systems with a collaborative filtering approach and a content-based deep learning method. Additionally, explore building a deep reinforcement learning model.
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