#BreakIntoAI with Machine Learning Specialization. Master fundamental AI concepts and develop practical
machine learning skills in the beginner-friendly, 3-course program by AI visionary Andrew Ng
- 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
| Decision Trees | Artificial Neural Network | Logistic Regression |
| Linear Regression | Regularization to Avoid Overfitting | Supervised Learning |
| Recommender Systems | Logistic Regression for Classification | Gradient Descent |
| Tensorflow | Tree Ensembles | Xgboost |
The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford
Online. This beginner-friendly program will teach you 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 version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over
4.8million 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 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.
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.