Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
If you are interested Machine Learning and looking for some excellent resources, then you have come to the right place . In this article, I am going to share some of the best online courses to learn Machine learning for programmers.
1. Machine Learning A-Z™: Hands-On Python & R In Data Science
Learn how to create Machine Learning Algorithms in Python programmingand R programming language from two Data Science experts.
The Author of the course is Kirill Eremenko, Hadelin de Ponteves .The moment when I write this article course has above 535,000+ students already enrolled, The course has 4.5 (105,265 ratings) out of 5 stars.
This course help you learn complex theory, algorithms and coding libraries in a simple way.Course will walk you 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 which 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.
2. Machine Learning, Data Science and Deep Learning with Python
This course was developed by Frank Kane a renowned instructor on Udemy. Currently, This course has nearly 115,200 students and excellent star ratings.
This comprehensive machine learning tutorial includes over 100 lectures spanning 14 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.
3. Machine Learning with Python and NLP
This online Machine Learning with Python and NLP course is taught by Veershetty Dagade . In this course the user will learn what machine learning is and also will learn how to develop machine learning Models. The course also includes various use cases for each topic where the student will get an opportunity to work with and map the learning experience to different use cases.
4. Python for Data Science and Machine Learning Bootcamp
Learn how to use Python NumPy, Pandas, Matplotlib, Plotly, Scikit-Learn, Machine Learning, Tensorflow, and more libraries and frameworks. The author of the course is Jose Portilla. The course had 292,000+ students enrolled.
The ratings for the course are 4.5 (61,862 ratings) out of 5, which is pretty impressive.
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!
this online course teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python!
5.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!
We'll teach you how to program with R, 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
- and much, much more!
Enroll in the course and become a data scientist today!
Conclusion
Machine Learning is currently one of the most prominent careers in the IT industry.If you want to stay ahead in the competition, then you had to take online machine learning courses.
In this article, I have listed the best machine learning courses from Udemy . If you want to work in big tech companies like Amazon, Facebook, Google, Microsoft, IBM, then you need to have deep knowledge of Artificial Intelligence, Machine Learning, and Deep Learning.
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