- Decision Trees
- Artificial Neural Network
- Logistic Regression
- Recommender Systems
- Linear Regression
- Regularization to Avoid Overfitting
- Gradient Descent
- Supervised Learning
- Logistic Regression for Classification
- Tree Ensembles
Machine Learning Specialization
#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
What you will learn
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
Skills you will gain
About this Specialization
Applied Learning Project
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.
How the Specialization Works
A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.
Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.
Earn a Certificate
When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.
There are 3 Courses in this Specialization
Advanced Learning Algorithms
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