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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.

Week 1 Learning Objective

• Build a neural network for binary classification of handwritten digits using TensorFlow.
• Gain a deeper understanding by implementing a neural network in Python from scratch.
• Optionally learn how neural network computations are “vectorized” to use parallel processing for faster training and prediction.

Week 2 Learning Objective

• Build a neural network to perform multi-class classification of handwritten digits in TensorFlow, using categorical cross-entropy loss functions and the softmax activation.
• Learn where to use different activation functions (ReLu, linear, sigmoid, softmax) in a neural network, depending on the task you want your model to perform.
• Use the advanced “Adam optimizer” to train your model more efficiently.

Week 3 Learning Objective

• Discover the value of separating your data set into training, cross-validation, and test sets.
• Choose from various versions of your model using a cross-validation dataset, and evaluate its ability to generalize to real-world data using a test dataset.
• Use “learning curves” to determine if your model is experiencing high bias or high variance (or both), and learn which techniques to apply (regularization, adding more data, adding or removing input features) to improve your model’s performance.
• Learn how the “bias-variance trade-off” is different in the age of deep learning, and apply Andrew Ng’s advice for handling bias and variance when training neural networks.
• Learn to apply the “iterative loop” of machine learning development to train, evaluate, and tune your model.
• Apply “data-centric AI” to not only tune your model but tune your data (using data synthesis or data augmentation) to improve your model’s performance.

Week 4 Learning Objective

• Build decision trees and tree ensembles, such as random forest and XGBoost (boosted trees) to make predictions.
• Learn when to use neural network or tree ensemble models for your task, as these are the two most commonly used supervised learning models in practice today.

In the third course of the Machine Learning Specialization, you will:

• Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.
• Build recommender systems with a collaborative filtering approach and a content-based deep learning method.
• Build a deep reinforcement learning model.

Week 1 Learning Objective

• Implement K-mean clustering.
• Implement anomaly detection.
• Learn how to choose between supervised learning or anomaly detection to solve certain tasks.

Week 2 Learning Objective

• Build a recommender system using collaborative filtering.
• Build a recommender system using a content-based deep learning method.

Week 3 Learning Objective

• Build a deep reinforcement learning model (Deep Q Network).