10 Best Machine Learning Courses and Certifications Online

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In this post, our specialists have actually assembled a curated list of the 10 Best of the Best Machine Learning courses, tutorials, training programs, classes and certifications that are offered online right now.

We have actually included just those courses that meet our high-quality requirements. We have actually put a lot of time and effort into gathering these all for you. These courses are suitable for all levels, beginners, intermediate students, and experts.

Here’s a look at these courses and what they have to offer for you!

10 Best Machine Learning Courses and Certifications Online

1. Machine Learning A-Z™: Hands-On Python & R In Data Science by “Kirill Eremenko, Hadelin de Ponteves, Ligency I Team, Ligency Team” Udemy Course Our Best Pick

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

As of right now, more than 888481+ people have enrolled in this course and there are over 161303+ reviews.

Course Content
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 by Jose Portilla Udemy Course

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

As of right now, more than 568581+ people have enrolled in this course and there are over 119090+ reviews.

Course Content
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
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 by Start-Tech Academy Udemy Course

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

As of right now, more than 354495+ people have enrolled in this course and there are over 4896+ reviews.

Course Content
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

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

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

As of right now, more than 166859+ people have enrolled in this course and there are over 27669+ reviews.

Course Content
“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

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5. Data Science and Machine Learning Bootcamp with R by Jose Portilla Udemy Course

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

As of right now, more than 83152+ people have enrolled in this course and there are over 15164+ reviews.

Course Content
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
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 by David Valentine Udemy Course

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

As of right now, more than 55859+ people have enrolled in this course and there are over 11895+ reviews.

Course Content
“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 by “Codestars by Rob Percival, Anthony NG, Rob Percival” Udemy Course

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

As of right now, more than 30748+ people have enrolled in this course and there are over 5317+ reviews.

Course Content
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 by Jose Portilla Udemy Course

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

As of right now, more than 29802+ people have enrolled in this course and there are over 5039+ reviews.

Course Content
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 by Lazy Programmer Inc. Udemy Course

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

As of right now, more than 29579+ people have enrolled in this course and there are over 5408+ reviews.

Course Content
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) by Chandra Lingam Udemy Course

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

As of right now, more than 25814+ people have enrolled in this course and there are over 3134+ reviews.

Course Content
Introduction and Housekeeping
SageMaker Housekeeping
Machine Learning Concepts
Model Performance Evaluation
SageMaker Service Overview
SageMaker Service and SDK Changes
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
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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How Long Does It Take to Learn Machine Learning?

The answer to the question “How long does it ttake to learn Machine Learning” is … it depends. Everyone has different requirements, and everyone is operating in different situations, so the answer for one person may be totally different than for somebody else.

Consider these questions: What are you trying to Learn Machine Learning for? Where is your beginning point? Are you a beginner or do you have experience with Machine Learning? How much can you practice? 1 hour each day? 40 hours each week? Take a look at this course about Machine Learning.

Is Machine Learning Easy Or Hard to Learn?

No, learning Machine Learning isn’t hard for the majority of people. Check this course on how to Learn Machine Learning in no time!

How to Learn Machine Learning Fast?

The fastest way to Learn Machine Learning is to first get this Machine Learning course, then practice whatever you learn whenever you can. Even if its just 15 minutes a day of practice. Consistency is key.

Where to Learn Machine Learning?

If you want to explore and learn Machine Learning, then Udemy offers you the best platform to learn the Machine Learning. Check this course on how to Learn Machine Learning in no time!