In the second course of the Machine Learning Specialization, you will:
• Build and train a neural network with TensorFlow to perform multi-class classification
• Apply best practices for machine learning development so that your models generalize to data and tasks in the real world
• Build and use decision trees and tree ensemble methods, including random forests and boosted trees
The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications.
This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.
This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.
It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)
By the end of this Specialization, you will have mastered key theoretical concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.
This week, you'll learn about neural networks and how to use them for classification tasks. You'll use the TensorFlow framework to build a neural network with just a few lines of code. Then, dive deeper by learning how to code up your own neural network in Python, "from scratch". Optionally, you ca…
This week, you'll learn how to train your model in TensorFlow, and also learn about other important activation functions (besides the sigmoid function), and where to use each type in a neural network. You'll also learn how to go beyond binary classification to multiclass classification (3 or more c…
This week you'll learn best practices for training and evaluating your learning algorithms to improve performance. This will cover a wide range of useful advice about the machine learning lifecycle, tuning your model, and also improving your training data.
This week, you'll learn about a practical and very commonly used learning algorithm the decision tree. You'll also learn about variations of the decision tree, including random forests and boosted trees (XGBoost).
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DeepLearning.AI is in Tier 2: excellent universities and the companies that build the technology of our institution ranking (90/100). Andrew Ng's courses.
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Dive into the intriguing world of AI Transformers with this curated YouTube playlist. Featuring expert insights, educational content from leading organizations, and engaging discussions, this playlist keeps you informed about the latest breakthroughs in Transformer technology.
