Machine Learning and Courses

Top 10 Machine Learning Courses to learn in 2021

Posted in Machine Learning, Courses
Machine Learning Courses

Professionals like Machine Learning Engineers and NLP Scientists are among the most promising and interesting career opportunities existing today. There's a countless supply of applications and industries where machine learning plays a major role in making them more intelligent and efficient. This subject is focused on helping the aspirants learn the mathematical aspects of machine learning algorithms and their utilization in programming languages.

If you are interested in building a career in this niche, consider checking the list below.

10 Best Machine Learning Courses

Here are the 10 best machine learning course.

1. Machine Learning Course by Stanford

Machine LearningThis program is designed to teach machine learning, support vector machines, kernels, neural networks, and related concepts using the best learning techniques. With this specialization, you'll learn the trick and techniques of AI and machine learning innovation processes.

Major topics covered:

  • Logistic Regression
  • Artificial Neural Network
  • Machine Learning Algorithms
  • Medical Informatics
  • Database Mining
  • Statistical Pattern Recognition
  • Regularization
  • Linear Regression With Multiple/One Variables

The course isn't limited to building foundations of machine learning in the students; it also teaches them the theoretical keystones of learning. The course will also teach you about the practical know-how of applying the techniques. Moreover, you'll gain access to some of Silicon Valley's innovation's best practices related to the subject.

Prerequisites: Background knowledge in Machine Learning or relevant subject isn't mandatory.

Level: Beginner
Rating: 4.9
Duration: 54 Hours (approximately)

You can signup here.

2. Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning A-Z™: Hands-On Python & R In Data ScienceThis course is the brainchild of expert Data Scientists who intend to help the individuals gain in-depth knowledge of complex algorithms and theory relevant to the subject. It is a well-structured learning program divided into two parts – Data Processing and Regression. The best part is that every tutorial is designed to help learners develop understanding and skills in Machine Learning.

It is a fun and exciting program to learn the following topics:

  • Data Preprocessing
  • Regression
  • Clustering
  • Association Rule Learning
  • Upper Confidence Bound and Thompson Sampling
  • Natural Language Processing
  • Artificial Neural Networks
  • Dimensionality Reduction
  • Convolutional Neural Networks
  • Parameter Tuning,
  • K-fold Cross-Validation
  • Grid Search, and more.

The course features real-life examples and practical exercises for the ease of students. Plus, you'll also gain some hands-on experience in creating your unique Machine Learning models. Students are facilitated with a bonus, R code, and Python templates that can be downloaded for various projects.

Prerequisites: No technical knowledge needed, except high school level math.
Level: Beginner
Rating: 4.5
Duration: 44 Hours (approximately)

You can signup here.

3. Machine Learning, Data Science, and Deep Learning with Python

Machine Learning Data Science and Deep Learning with PythonThe specialization covers all the major topics related to machine learning, including artificial neural networks, K-Means Clustering, etc. Additionally, you'll learn the technicalities of Data Visualization with Seaborn and MatPlotLib along with the practical implementation of machine learning at a large scale with MLLib Apache Spark.

Major topics featured in this course:

  • Neural Networks and Deep Learning with Keras and TensorFlow
  • Transfer Learning
  • Image classification and recognition
  • Sentiment analysis
  • Multi-Level Models
  • Regression analysis
  • Multiple Regression
  • Random Forests and Decision Trees
  • A/B Tests and Experimental Design
  • Collaborative Filtering
  • Reinforcement Learning
  • Support Vector Machines
  • Feature Engineering
  • Hyperparameter Tuning, and more.

With this course, you'll also learn to classify sentiments, images, and data using deep learning concepts. It is an ideal learning program for professional programmers and data analysts intending to switch their careers. You can opt for this specialization even if you are new to Python as it features a crash course for a better understanding of the subject.

Prerequisites:

  • Linux, Mac, or Windows computer that can run new versions like Anaconda 3.
  • Prior experience in scripting or programming is mandatory.
  • You should be skilled in high school level mathematics.

Level: Intermediate
Rating:4.5
Duration: 14 hours (approximately)

You can signup here.

4. Machine Learning with Javascript

Machine Learning with JavascriptDesigned for the Javascript developers, this Machine Learning course will take you into the depths of advanced memory profiling, building Tensorflow JS library powered apps, writing ML code, and other major topics relevant for a thorough understanding of the subject.

Additionally, you'll also learn to create programs compatible with both Node JS and browser. The program also teaches the tricks and techniques of speeding up matrix-based codes with Linear Algebra basics.

The primary topics featured in this course:

  • Identifying Relevant Data
  • Recording Observation Data
  • Algorithms Overview
  • Tensor Concatenation
  • Applications of Tensorflow
  • Linear Regression
  • Matrix Multiplication
  • Vectorized Solutions for increasing performance
  • Plotting MSE Values with Javascript
  • Logistic Regression
  • Stochastic and Batch Gradient Descent

In addition to the above, you'll learn to alter the algorithms according to the use cases. Moreover, the course will give you access to the performance-enhancing techniques and strategies for the Javascript code. The most amazing part is that you can go for this course even if you don't have any mathematics background as the lectures don't involve tough math concepts.

Prerequisites:

  • Fundamental knowledge of command and terminal line usage.
  • The capability of handling basic equations of math.

Level: Intermediate
Rating: 4.7
Duration:17.5 hours (approximately)

You can signup here.

5. The Complete Machine Learning Course with Python

The Complete Machine Learning Course with PythonIf you are looking for a course that can help you build a strong foundation in Machine Learning, then end your search with this program. You'll learn to differentiate between machine learning and classical programming, deep learning, and machine learning. Plus, you'll also acquire knowledge about neural networks, tensor operations, and advanced topics such as validation, dropout, testing, regularization, under and overfitting.

This learning program provides detailed insight into the following topics:

  • Linear Regression with Scikit-Learn
  • Robust Regression
  • Data Preprocessing
  • Cross-validation
  • Logistic Regression
  • Confusion Matrix
  • Concepts of Support Vector Machine
  • Radial Basis Function
  • Linear SVM Classification
  • Visualizing Boundary
  • Ensemble Machine Learning Methods
  • Gradient Boosting Machine
  • kNN introduction
  • Dimensionality Reduction Concept
  • Clustering

You'll acquire a good understanding of machine learning tools used for tackling real-world issues. It's a perfect course to learn about ML performance metrics, including recall, R-squared, confusion matrix, MSE, prevision, accuracy, and more.

Prerequisites:

  • Fundamental knowledge of Python programming is required.
  • Experience in linear algebra.

Level: Beginner-Intermediate
Rating: 4.3
Duration: 17.5 Hours (approximately)

You can signup here.

6. Data Science: Machine Learning

Data Science: Machine LearningOffered by Harvard University, this specialization is created to help the aspirants learn machine learning and the technical problems associated with it. Unlike other courses, this learning program will help you dig deeper into ML's data science methodologies.

The following are the core topics featured in this course:

  • Machine Learning Basics
  • Principal Component Analysis
  • Machine Learning Algorithms
  • Building Recommendation System
  • Regularization and its uses
  • Cross-Validation

The program also offers knowledge of training data and efficient ways of using data set for discovering predictive relationships. On signing up for this course, you'll also know about implementing machine learning in various products such as speech recognition, postal service, spam detectors, etc.

Prerequisites: None
Level: Beginner
Rating: 4.3
Duration: 8 weeks – 2-4 hours per week (approximately)

You can signup here.

7. Intro to Machine Learning with PyTorch

Intro to Machine Learning with PyTorchAvailable at Udacity, this Nanodegree program is an ideal option to enhance your skills and knowledge in supervised models, data cleaning, and machine learning algorithms. Additionally, candidates can also explore other important topics like unsupervised and deep learning. The course is divided into different steps, with each one offering practical experience to the learners where they can test their skills through code projects and exercises.

The course is inclusive of the following topics:

  • Model Construction
  • Neural Network Design
  • Pytorch Training
  • Unsupervised Learning Method Implementation
  • Deep Learning

The specialization offers the experience of handling real projects where candidates learn to create immersive content for top tier organizations. Plus, you'll also attain a major in the relevant tech skills. The learners are also guided for various training sessions, interview preparation, professional profile maintenance, and other crucial areas for career growth.

Prerequisites: Fundamental knowledge of Python programming is needed.
Level: Intermediate (3 months access)
Rating: 4.3
Duration- 3 months / 10 hours per week (approximately)

You can signup here.

8. Introduction to Machine Learning for Coders

Introduction to Machine Learning for Coders!This course teaches the mechanisms of machine learning from scratch to equip students with a thorough understanding of theoretical and practical usage of Machine Learning models.

The course covers topic such as:

  • Introduction and in-depth understanding of the Random forest.
  • Applications of the model validation process.
  • Learning Model interpretation, Tree interpreters, Data products, and Gradient Descent.
  • Understanding Logistic regression and using it to solve various complex issues.
  • Complete understanding and implementation of Neuro-Linguistic Programming and columnar data.

Upon completing the course, students can create their own Machine Learning Models for commercial purposes and apply practical knowledge to advance their technical analysis career.

Prerequisites: Knowledge of high school mathematics and practical coding experience.

Level: Beginner
Rating: 4.3
Duration: 24 hours

You can sign up here.

9. Introduction to Machine Learning Course

Introduction to Machine Learning CourseThis Machine learning program will help you master the subject's core areas, including statistics and computer science, to efficiently leverage the predictive power. It's an ideal course for aspiring data scientists, data analysts, and others who want to build their career in the relevant field. You'll learn about the details of data investigating processes through the lens of machine learning.

The course is created to help candidates learn the following concepts:

  • Use of Naïve Bayes
  • Posterior Probability Calculation
  • Support Vector Machines
  • Coding Decision tree using Python
  • Choosing a Machine Learning Algorithm
  • Enron Email Dataset Patterns
  • Regressions and Outliners
  • Clustering and Scaling

In addition to the above, you'll also learn how to extract and identify useful Machine Learning features for the best representation of the data. The course offers a rich learning experience to the candidates through its professionally designed syllabus. Plus, there are interactive quizzes for the students to test their skills and knowledge in the subject.

Prerequisites: Background in Machine Learning or relevant experience is needed.

Level: Intermediate
Rating: 4.5
Duration:10 weeks (approximately)

You can signup here.

10. Advanced Machine learning Specialization

Advanced Machine Learning SpecializationThis course covers an advanced understanding of Modern Artificial Intelligence techniques to program computers to interact and analyze and solve problems. You will learn the inner workings of AI technologies based on which today’s AI models are built.

The course covers topic such as:

  • Learning and Implementing Data Analysis and Gaussian process.
  • Concepts in Deep Learning, Recurrent Neural Networks, Convolutional Neural Networks.
  • Understanding of Bayesian Optimization and variations in Bayesian Techniques.
  • Working with Xgboost.
  • Learning Feature Extraction and Engineering.
  • Working with Markov Chain Monte Carlo Model.

Upon completing the course, students can apply for advanced machine learning jobs to create projects to solve real-world problems. These techniques can also be used to design progressive AI models.

Prerequisites: Knowledge of programming, mathematics, and statistics.

Level: Advanced
Rating: 4.6
Duration: Approximately 10 months

You can sign up here.

Conclusion

Machine learning is a fun and interesting subject to learn that allows individuals to boundlessly experiment with their skills and knowledge. To build a career in this field, start with gaining complete knowledge of ML and the related concepts. Pick any of the specializations listed above to start your journey. These Machine Learning courses are not only cost-effective but also offer the flexibility of learning anywhere, anytime.

Come back and share your experiences with us and suggest to the rest of the community, which course you like and why? Have any other course to recommend? Comment below!

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Aditi Jhalani

Aditi Jhalani

Aditi Jhalani, holds years of experience and has a special interest in writing on technical subjects. Not just these, the writer enjoys writing for educational, IT, fashion and numerous other subjects too. She was previously a fashion designer but discovered that penning down her thoughts on paper was far more interesting. She has worked with many prestigious organizations like FabFurnish, IHPL, Toppr, Hackr, Malabar Gold n Diamonds and more in the recent years and produced reader-friendly content. View all posts by the Author

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