In this article, you will find the **Best Machine Learning Courses on Udemy** & advanced like Beginner courses, and Practice test courses**. **So, check these **Best Machine Learning Courses on Udemy** and find the **Best **Machine Learning** Courses on Udemy for Beginners** **to Advanced** according to your need. In the previous article, I have shared the **Best Machine Learning Books** **for Beginners** to advance that help you get practical skills with those courses.

Here we have covered the Best Machine Learning Courses on Udemy. Let’s go through the list of Udemy Machine Learning Courses one by one.

## Best Machine Learning Courses on Udemy 2022

### Machine Learning A-Z: Hands-On Python & R In Data Science

This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.

You will walk with step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured the following way:

- Part 1 – Data Preprocessing
- Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
- Part 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
- Part 4 – Clustering: K-Means, Hierarchical Clustering
- Part 5 – Association Rule Learning: Apriori, Eclat
- Part 6 – Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
- Part 7 – Natural Language Processing: Bag-of-words model and algorithms for NLP
- Part 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
- Part 9 – Dimensionality Reduction: PCA, LDA, Kernel PCA
- Part 10 – Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.

And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.

**Best for:** Beginners and Intermediate | **Course duration:** 44hrs | **Provider:** Udemy

**Total Articles:** 44 | **Downloadable Resources:** 38

Info: **Visit this course and get amazing offers on this course**

### Machine Learning, Data Science and Deep Learning with Python

this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry – and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over **100 lectures** spanning **15 hours of video**, and most topics include **hands-on Python code examples** you can use for reference and for practice.

Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won’t find academic, deeply mathematical coverage of these algorithms in this course – the focus is on practical understanding and application of them. At the end, you’ll be given a **final project** to apply what you’ve learned!

The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers.

- Deep Learning / Neural Networks (MLP’s, CNN’s, RNN’s) with TensorFlow and Keras
- Creating synthetic images with Variational Auto-Encoders (VAE’s) and Generative Adversarial Networks (GAN’s)
- Data Visualization in Python with MatPlotLib and Seaborn
- Transfer Learning
- Sentiment analysis
- Image recognition and classification
- Regression analysis
- K-Means Clustering
- Principal Component Analysis
- Train/Test and cross validation
- Bayesian Methods
- Decision Trees and Random Forests
- Multiple Regression
- Multi-Level Models
- Support Vector Machines
- Reinforcement Learning
- Collaborative Filtering
- K-Nearest Neighbor
- Bias/Variance Tradeoff
- Ensemble Learning
- Term Frequency / Inverse Document Frequency
- Experimental Design and A/B Tests
- Feature Engineering
- Hyperparameter Tuning

**Best for:** Beginners and Intermediate | **Course duration:** 15.5hrs | **Provider:** Udemy

**Total Articles:** 6 | **Downloadable Resources:** 1

Info: **Visit this course and get amazing offers on this course**

### Python for Data Science and Machine Learning Bootcamp

This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!

This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With **over 100 HD video lectures** and **detailed code notebooks for every lecture **this is one of the most comprehensive course for data science and machine learning on Udemy!

how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning:

- Programming with Python
- NumPy with Python
- Using pandas Data Frames to solve complex tasks
- Use pandas to handle Excel Files
- Web scraping with python
- Connect Python to SQL
- Use matplotlib and seaborn for data visualizations
- Use plotly for interactive visualizations
- Machine Learning with SciKit Learn, including:
- Linear Regression
- K Nearest Neighbors
- K Means Clustering
- Decision Trees
- Random Forests
- Natural Language Processing
- Neural Nets and Deep Learning
- Support Vector Machines

**Best for:** Beginners and Intermediate | **Course duration:** 25hrs | **Provider:** Udemy

**Total Articles:** 13 | **Downloadable Resources:** 5

Info: **Visit this course and get amazing offers on this course**

### Data Science and Machine Learning Bootcamp with R

This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science!

This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With **over 100 HD video lectures** and **detailed code notebooks for every lecture **this is one of the most comprehensive course for data science and machine learning on Udemy!

, how to create amazing data visualizations, and how to use Machine Learning with R! Here a just a few of the topics we will be learning:

- Programming with R
- Advanced R Features
- Using R Data Frames to solve complex tasks
- Use R to handle Excel Files
- Web scraping with R
- Connect R to SQL
- Use ggplot2 for data visualizations
- Use plotly for interactive visualizations
- Machine Learning with R, including:
- Linear Regression
- K Nearest Neighbors
- K Means Clustering
- Decision Trees
- Random Forests
- Data Mining Twitter
- Neural Nets and Deep Learning
- Support Vectore Machines

**Best for:** Beginners | **Course duration:** 17.5hrs | **Provider:** Udemy

**Total Articles:** 9 | **Downloadable Resources:** 3

Info: **Visit this course and get amazing offers on this course**

### The Complete Machine Learning Course with Python

With **over 18 hours of content and more than fifty 5 star ratings**, it’s already the longest and best rated Machine Learning course on Udemy!

**Build Powerful Machine Learning Models to Solve Any Problem**

You’ll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.

By the end of the course, you will have trained machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more!

**Inside the course, you’ll learn how to:**

- Gain
**complete machine learning tool sets**to tackle most real world problems - Understand the various
**regression, classification and other ml algorithms**performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them. - Combine multiple models with by
**bagging, boosting or stacking** - Make use to
**unsupervised Machine Learning**(ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data - Develop in
**Jupyter (IPython) notebook, Spyder and various IDE** - Communicate visually and effectively with
**Matplotlib**and**Seaborn** - Engineer new features to
**improve algorithm predictions** - Make use of t
**rain/test, K-fold and Stratified K-fold cross validation**to select correct model and predict model perform with unseen data - Use
**SVM**for handwriting recognition, and classification problems in general - Use
**decision trees**to predict staff attrition - Apply the
**association rule**to retail shopping datasets

**Best for:** Beginners | **Course duration:** 17.5hrs | **Provider:** Udemy

**Total Articles:** 3 | **Downloadable Resources:** 2

Info: **Visit this course and get amazing offers on this course**

### Machine Learning & Deep Learning in Python & R

This course covers all the steps that one should take while solving a business problem through linear regression. It also focuses Machine Learning and Deep Learning techniques in R and Python.

Most courses only focus on teaching how to run the data analysis but we believe that what happens before and after running data analysis is even more important i.e. before running data analysis it is very important that you have the right data and do some pre-processing on i

. And after running data analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business. Here comes the importance of machine learning and deep learning. Knowledge on data analysis tools like R, Python play an important role in these fields of Machine Learning and Deep Learning.

By the end of this course, your confidence in creating a Machine Learning or Deep Learning model in Python and R will soar. You’ll have a thorough understanding of how to use ML/ DL models to create predictive models and solve real world business problems.

- Learn how to solve real life problem using the Machine learning techniques
- Machine Learning models such as Linear Regression, Logistic Regression, KNN etc.
- Advanced Machine Learning models such as Decision trees, XGBoost, Random Forest, SVM etc.
- Understanding of basics of statistics and concepts of Machine Learning
- How to do basic statistical operations and run ML models in Python
- In-depth knowledge of data collection and data preprocessing for Machine Learning problem
- How to convert business problem into a Machine learning problem

**Best for:** Beginners | **Course duration:** 33hrs | **Provider:** Udemy

**Total Articles:** 4 | **Downloadable Resources:** 4

Info: **Visit this course and get amazing offers on this course**

### Machine Learning and Data Science Hands-on with Python and R

This program will help you build the foundation for a solid career in Machine learning Tools. Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions.

you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.

- Learn Deep Learning, R Programming, NLP, Bayesian Methods, A/B Testing, Business Intelligence BI, Regression.
- Learn Hypothesis Testing, Algebra, Adaboost Regressor, Gaussian, Heuristic.
- Learn Numpy, Pandas, Metplotlit, Seaborn.
- Learn Forecasting, Distribution, Normalization, Trend Analysis, Predictive Modeling, Fraud Detection.
- Learn Neural Network, Sequential Model, Data Visualization, Data Analysis, Data Manipulation, KNN Algorithm.
- Learn Decision Tree, Random Forests, Kmeans Clustering, Vector Machine, Time Series Analysis, Market Basket Analysis

**Best for:** Beginners | **Course duration:** 72.5hrs | **Provider:** Udemy

**Downloadable Resources:** 1

Info: **Visit this course and get amazing offers on this course**

### 2022 Machine Learning A to Z : 5 Machine Learning Projects

This course will take you step by step into the world of **Machine Learning**.

This Machine Learning course will give you theoretical as well as practical knowledge of Machine Learning.This Machine Learning course is fun as well as exciting. It will cover all common and important algorithms and will give you the experience of working on some real-world projects.

In this Python Machine Learning Employee Promotion Prediction project, you will learn how to Implement a Predictive Model for Identifying the Right Employees deserving of Promotion. Also, learn how to balance Imbalanced Datasets.

In this Python Machine Learning Predicting Medical Health Expenses project, you will learn how to Implement a Regression Analysis Predictive Model for Predicting the Future Medical Expenses for People using Linear Regression, Random Forest, Gradient Boosting, etc.

In this Python Machine Learning Determining Status for Loan Applicants project, you will learn how to Implement a Classification Analysis Predictive Model for Determining whether a Person should be Granted a Loan or Not.

In this Python Machine Learning Optimizing Crop Production project, you will learn about Precision Farming using Data Science Technologies such as Clustering Analysis and Classification Analysis. You will be able to Recommend the best Crops to Farmers to Increase their Productivity.

This course will cover the following topics:-

- Theory and practical implementation of linear regression using sklearn.
- Theory and practical implementation of logistic regression using sklearn.
- Feature selection using RFECV.
- Data transformation with linear and logistic regression.
- Evaluation metrics to analyze the performance of models.
- Industry relevance of linear and logistic regression.
- Mathematics behind KNN, SVM, and Naive Bayes algorithms.
- Implementation of KNN, SVM, and Naive Bayes using sklearn.
- Attribute selection methods- Gini Index and Entropy.
- Mathematics behind Decision trees and random forest.
- Boosting algorithms:- Adaboost, Gradient Boosting, and XgBoost.
- Different algorithms for clustering.
- Different methods to deal with imbalanced data.
- Correlation filtering.
- Variance filtering.
- PCA & LDA.
- Content and Collaborative based filtering.
- Singular Value decomposition.
- Different algorithms used for Time Series forecasting.
- Case studies.

**Best for:** Beginners | **Course duration:** 26hrs | **Provider:** Udemy

**Total Articles:** 3 | **Downloadable Resources:** 213

Info: **Visit this course and get amazing offers on this course**

And here the list ends. So, these are the** Best Machine Learning Courses on Udemy for Beginners to Advanced**. I will keep adding more Best Machine Learning on Udemy to this list.

**Conclusion**

I hope these **Best Machine Learning Courses on Udemy for Beginners to Advanced **will definitely help you to enhance your skills. If you have any doubts or questions, feel free to ask me in the comment section.