• Python
• Regression
• Classification
• Recommendation System
• Artificial Neural Network
• ... And more!!!

• Build ML models with NumPy & scikit-learn, build & train supervised models for prediction & binary classification tasks (linear, logistic regression)
• Build & train a neural network with TensorFlow to perform multi-class classification, & build & use decision trees & tree ensemble methods
• Apply best practices for ML development & use unsupervised learning techniques for unsupervised learning including clustering & anomaly detection
• Build recommender systems with a collaborative filtering approach & a content-based deep learning method & build a deep reinforcement learning model

By the end of this Specialization, you will be ready to:

• Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.
• Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression.
• 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.
• 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.

In the first course of the specialization, you'll:

• Have a good understanding of the concepts of Supervised Learning, Unsupervised Learning, Regression, Classification, Clustering, Gradient Descent,...
• Build simple machine learning models in Python using popular machine learning libraries NumPy & scikit-learn.
• Build & train supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression.

In the second course of the specialization, you'll able to:

• 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.

In the last course of the specialization, you'll be able to:

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

1. Machine Learning Specialization
2. Supervised Machine Learning: Regression and Classification
3. Advanced Learning Algorithms
4. Unsupervised Learning, Recommenders, Reinforcement Learning