Machine Learning

开始时间: 12/21/2023 持续时间: Approximately 2 months to complete Suggested pace of 8 hours/week

所在平台: Coursera专项课程

课程主页: https://www.coursera.org/specializations/machine-learning-introduction

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课程详情

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
Decision Trees
Artificial Neural Network
Logistic Regression
Recommender Systems
Linear Regression
Regularization to Avoid Overfitting
Gradient Descent
Supervised Learning
Logistic Regression for Classification
Xgboost
Tensorflow
Tree Ensembles
About this Specialization
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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 4.8 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 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.
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.
Shareable Certificate
Shareable Certificate
Earn a Certificate upon completion
100% online courses
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Flexible Schedule
Set and maintain flexible deadlines.
Beginner Level
Beginner Level
Basic coding (for loops, functions, if/else statements) & high school-level math (arithmetic, algebra)
Other math concepts will be explained
Hours to complete
Approximately 2 months to complete
Suggested pace of 8 hours/week
Available languages
English
Subtitles: English
Shareable Certificate
Shareable Certificate
Earn a Certificate upon completion
100% online courses
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Flexible Schedule
Set and maintain flexible deadlines.
Beginner Level
Beginner Level
Basic coding (for loops, functions, if/else statements) & high school-level math (arithmetic, algebra)
Other math concepts will be explained
Hours to complete
Approximately 2 months to complete
Suggested pace of 8 hours/week
Available languages
English
Subtitles: English

课程大纲

Course Link: https://www.coursera.org/learn/machine-learning

Name:Supervised Machine Learning: Regression and Classification

Description:Offered by Stanford University and DeepLearning.AI. In the first course of the Machine Learning Specialization, you will: • Build machine ... Enroll for free.

Course Link: https://www.coursera.org/learn/advanced-learning-algorithms

Name:Advanced Learning Algorithms

Description:Offered by Stanford University and DeepLearning.AI. In the second course of the Machine Learning Specialization, you will: • Build and train ... Enroll for free.

Course Link: https://www.coursera.org/learn/unsupervised-learning-recommenders-reinforcement-learning

Name:Unsupervised Learning, Recommenders, Reinforcement Learning

Description:Offered by Stanford University and DeepLearning.AI. In the third course of the Machine Learning Specialization, you will: • Use unsupervised ... Enroll for free.

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课程简介

吴恩达老师最新的机器学习专项课程(Machine Learning Specialization) ,机器学习入门首选课程。这个专项课程是他早期机器学习课程的最新替代版本,包含3门子课程,涵盖有监督学习(回归、分类问题等)、高级学习算法(神经网络、决策树、随机森林等)、无监督学习、推荐系统和强化学习等主题,感兴趣的同学可以关注:Break Into AI 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

课程标签

机器学习 吴恩达 机器学习专项

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