10最好的机器学习教程推荐

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特写 iPhone,显示 Udemy 应用程序和带笔记本的笔记本电脑有数以千计的在线课程和课程可以帮助您提高 机器学习 技能并获得 机器学习 证书。

在这篇博客文章中,我们的专家汇总了 10 个精选列表 最好的 机器学习 课程, 现在在线提供的教程、培训计划、课程和认证。

我们只包括那些符合我们高质量标准的课程。我们花了很多时间和精力来为您收集这些。这些课程适合所有级别的初学者、中级学习者和专家。

以下是这些课程以及它们为您提供的内容!

10最好的机器学习教程推荐

1. Machine Learning A-Z™: Hands-On Python & R In Data Science 经过 “Kirill Eremenko, Hadelin de Ponteves, Ligency I Team, Ligency Team” Udemy课程 我们的最佳选择

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

截至目前,超过 888481+ 人们已经注册了这门课程,而且已经结束了 161303+ 评论.

课程内容
Welcome to the course! Here we will help you get started in the best conditions.
——————– Part 1: Data Preprocessing ——————–
Data Preprocessing in Python
Data Preprocessing in R
——————– Part 2: Regression ——————–
Simple Linear Regression
Multiple Linear Regression
Polynomial Regression
Support Vector Regression (SVR)
Decision Tree Regression
Random Forest Regression
Evaluating Regression Models Performance
Regression Model Selection in Python
Regression Model Selection in R
——————– Part 3: Classification ——————–
Logistic Regression
K-Nearest Neighbors (K-NN)
Support Vector Machine (SVM)
Kernel SVM
Naive Bayes
Decision Tree Classification
Random Forest Classification
Classification Model Selection in Python
Evaluating Classification Models Performance
——————– Part 4: Clustering ——————–
K-Means Clustering
Hierarchical Clustering
——————– Part 5: Association Rule Learning ——————–
Apriori
Eclat
——————– Part 6: Reinforcement Learning ——————–
Upper Confidence Bound (UCB)
Thompson Sampling
——————– Part 7: Natural Language Processing ——————–
——————– Part 8: Deep Learning ——————–
Artificial Neural Networks
Convolutional Neural Networks
——————– Part 9: Dimensionality Reduction ——————–
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
Kernel PCA
——————– Part 10: Model Selection & Boosting ——————–
Model Selection
XGBoost
Bonus Lectures

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2. Python for Data Science and Machine Learning Bootcamp 经过 Jose Portilla Udemy课程

“Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!”

截至目前,超过 568581+ 人们已经注册了这门课程,而且已经结束了 119090+ 评论.

课程内容
Course Introduction
Environment Set-Up
Jupyter Overview
Python Crash Course
Python for Data Analysis – NumPy
Python for Data Analysis – Pandas
Python for Data Analysis – Pandas Exercises
Python for Data Visualization – Matplotlib
Python for Data Visualization – Seaborn
Python for Data Visualization – Pandas Built-in Data Visualization
Python for Data Visualization – Plotly and Cufflinks
Python for Data Visualization – Geographical Plotting
Data Capstone Project
Introduction to Machine Learning
Linear Regression
Cross Validation and Bias-Variance Trade-Off
Logistic Regression
K Nearest Neighbors
Decision Trees and Random Forests
Support Vector Machines
K Means Clustering
Principal Component Analysis
Recommender Systems
Natural Language Processing
Neural Nets and Deep Learning
Big Data and Spark with Python
BONUS SECTION: THANK YOU!

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3. Machine Learning & Deep Learning in Python & R 经过 Start-Tech Academy Udemy课程

“Covers Regression, Decision Trees, SVM, Neural Networks, CNN, Time Series Forecasting and more using both Python & R”

截至目前,超过 354495+ 人们已经注册了这门课程,而且已经结束了 4896+ 评论.

课程内容
Introduction
Setting up Python and Jupyter Notebook
Setting up R Studio and R crash course
Basics of Statistics
Introduction to Machine Learning
Data Preprocessing
Linear Regression
Introduction to the classification Models
Logistic Regression
Linear Discriminant Analysis (LDA)
K-Nearest Neighbors classifier
Comparing results from 3 models
Simple Decision Trees
Simple Classification Tree
Ensemble technique 1 – Bagging
Ensemble technique 2 – Random Forests
Ensemble technique 3 – Boosting
Support Vector Machines
Support Vector Classifier
Support Vector Machines
Creating Support Vector Machine Model in Python
Creating Support Vector Machine Model in R
Introduction – Deep Learning
Neural Networks – Stacking cells to create network
ANN in Python
ANN in R
CNN – Basics
Creating CNN model in Python
Creating CNN model in R
Project : Creating CNN model from scratch in Python
Project : Creating CNN model from scratch
Project : Data Augmentation for avoiding overfitting
Transfer Learning : Basics
Transfer Learning in R
Time Series Analysis and Forecasting
Time Series – Preprocessing in Python
Time Series – Important Concepts
Time Series – Implementation in Python
Time Series – ARIMA model
Time Series – SARIMA model
Congratulations & About your certificate

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4. “Machine Learning, Data Science and Deep Learning with Python” 经过 “Sundog Education by Frank Kane, Frank Kane, Sundog Education Team” Udemy课程

“Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks”

截至目前,超过 166859+ 人们已经注册了这门课程,而且已经结束了 27669+ 评论.

课程内容
“Getting Started
Statistics and Probability Refresher, and Python Practice
Predictive Models
Machine Learning with Python
Recommender Systems
More Data Mining and Machine Learning Techniques
Dealing with Real-World Data
Apache Spark: Machine Learning on Big Data
Experimental Design / ML in the Real World
Deep Learning and Neural Networks
Generative Models
Final Project
You made it!”

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5. Data Science and Machine Learning Bootcamp with R 经过 Jose Portilla Udemy课程

Learn how to use the R programming language for data science and machine learning and data visualization!

截至目前,超过 83152+ 人们已经注册了这门课程,而且已经结束了 15164+ 评论.

课程内容
Course Introduction
Course Best Practices
Windows Installation Set-Up
Mac OS Installation Set-Up
Linux Installation
Development Environment Overview
Introduction to R Basics
R Matrices
R Data Frames
R Lists
Data Input and Output with R
R Programming Basics
Advanced R Programming
Data Manipulation with R
Data Visualization with R
Data Visualization Project
Interactive Visualizations with Plotly
Capstone Data Project
Introduction to Machine Learning with R
Machine Learning with R – Linear Regression
Machine Learning Project – Linear Regression
Machine Learning with R – Logistic Regression
Machine Learning Project – Logistic Regression
Machine Learning with R – K Nearest Neighbors
Machine Learning Project – K Nearest Neighbors
Machine Learning with R – Decision Trees and Random Forests
Machine Learning Project – Decision Trees and Random Forests
Machine Learning with R – Support Vector Machines
Machine Learning Project – Support Vector Machines
Machine Learning with R – K-means Clustering
Machine Learning Project – K-means Clustering
Machine Learning with R – Natural Language Processing
Machine Learning with R – Neural Nets
Machine Learning Project – Neural Nets
Bonus Section

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6. Introduction to Machine Learning for Data Science 经过 David Valentine Udemy课程

“A primer on Machine Learning for Data Science. Revealed for everyday people, by the Backyard Data Scientist.”

截至目前,超过 55859+ 人们已经注册了这门课程,而且已经结束了 11895+ 评论.

课程内容
“Introduction
Core Concepts
Impacts, Importance and examples
The Machine Learning Process
How to apply Machine Learning for Data Science
Conclusion
Section 1 -Bonus course – Machine Learning in Python and Jupyter for Beginners
Section 2 -Bonus course – Machine Learning in Python and Jupyter for Beginners
Section 3 – Bonus course – Machine Learning in Python and Jupyter for Beginners
Section 4 – Bonus course – Machine Learning in Python and Jupyter for Beginners
Section 5 -Bonus course – Machine Learning in Python and Jupyter for Beginners
Section 6 – Bonus course – Machine Learning in Python and Jupyter for Beginners
Section 7 -Bonus course – Machine Learning in Python and Jupyter for Beginners
Bonus Content”

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7. The Complete Machine Learning Course with Python 经过 “Codestars by Rob Percival, Anthony NG, Rob Percival” Udemy课程

“Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!”

截至目前,超过 30748+ 人们已经注册了这门课程,而且已经结束了 5317+ 评论.

课程内容
Introduction
Getting Started with Anaconda
Regression
Classification
Support Vector Machine (SVM)
Tree
Ensemble Machine Learning
k-Nearest Neighbours (kNN)
Unsupervised Learning: Dimensionality Reduction
Unsupervised Learning: Clustering
Deep Learning
Appendix A1: Foundations of Deep Learning
Computer Vision and Convolutional Neural Network (CNN)

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8. Scala and Spark for Big Data and Machine Learning 经过 Jose Portilla Udemy课程

“Learn the latest Big Data technology – Spark and Scala, including Spark 2.0 DataFrames!”

截至目前,超过 29802+ 人们已经注册了这门课程,而且已经结束了 5039+ 评论.

课程内容
Course Introduction
Scala IDE Options and Overview
Windows Scala and Spark Set-up and Installation
Mac OS Setup and Installation
Linux (Ubuntu) Setup and Installation
Scala Programming: Level One
Collections
Scala Programming: Level Two
Spark DataFrames with Scala
Introduction to Machine Learning
Regression with Spark
Classification with Spark
Model Evaluation
Clustering with Spark
PCA with Spark
DataBricks and Spark
BONUS SECTION: THANK YOU!

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9. Deep Learning Prerequisites: Linear Regression in Python 经过 Lazy Programmer Inc. Udemy课程

“Data science, machine learning, and artificial intelligence in Python for students and professionals”

截至目前,超过 29579+ 人们已经注册了这门课程,而且已经结束了 5408+ 评论.

课程内容
Welcome
1-D Linear Regression: Theory and Code
Multiple linear regression and polynomial regression
Practical machine learning issues
Conclusion and Next Steps
Setting Up Your Environment (FAQ by Student Request)
Extra Help With Python Coding for Beginners (FAQ by Student Request)
Effective Learning Strategies for Machine Learning (FAQ by Student Request)
Appendix / FAQ Finale

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10. AWS Certified Machine Learning Specialty (MLS-C01) 经过 Chandra Lingam Udemy课程

Hands on AWS ML SageMaker Course with Practice Test. Join Live Study Group Q&A!

截至目前,超过 25814+ 人们已经注册了这门课程,而且已经结束了 3134+ 评论.

课程内容
Introduction and Housekeeping
SageMaker Housekeeping
Machine Learning Concepts
Model Performance Evaluation
SageMaker Service Overview
SageMaker Service and SDK Changes
XGBoost – Gradient Boosted Trees
Invoke Model Endpoint from External Clients
Endpoint Changes with Zero Downtime
Emerging AI Trends and Social Issues
Cloud Security and Access Management
Principal Component Analysis (PCA)
Recommender Systems – Factorization Machines
Model Optimization and HyperParameter Tuning
Time Series Forecasting – DeepAR
Anomaly Detection – Random Cut Forest
Artificial Intelligence (AI) Services
S3 Data Lake Architecture – Data Consolidation
Deep Learning and Neural Networks
Bring Your Own Algorithm
Storage for Servers
AWS – Support Plans and Feedback
Databases on AWS
On-Premises usage and other technologies
Practice Exam – AWS Certified Machine Learning Specialty
Other Resources

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下面是一些关于学习的常见问题机器学习

学习机器学习需要多长时间?

“学习机器学习需要多长时间”这个问题的答案是。 . .这取决于。每个人都有不同的需求,每个人都在不同的场景下工作,所以一个人的答案可能与另一个人的答案完全不同。

考虑这些问题:你想学习 机器学习 是为了什么?你的出发点在哪里?您是初学者还是有使用 机器学习 的经验?你能练习多少?每天1小时?每周40小时? 查看本课程关于 机器学习.

机器学习 学起来容易还是难?

不,学习 机器学习 对大多数人来说并不难。检查这个 关于如何学习的课程 机器学习 立刻!

如何快速学习机器学习?

学习 机器学习 最快的方法是先得到这个 机器学习 课程, 然后尽可能练习你学到的任何东西。即使每天只有 15 分钟的练习。一致性是关键.

在哪里学习 机器学习?

如果您想探索和学习 机器学习,那么 Udemy 为您提供了学习 机器学习 的最佳平台。查看此 关于如何学习的课程 机器学习 立刻!