So you have decided to learn machine learning and start a promising career in this domain. Probably you are relying on online education as taking offline courses does not seem feasible for working employees. So, in this article, we have shared some of the best courses available online to help you build a strong foundation in machine learning.
Here goes the list!
- Machine Learning Certification Course
Training Provider – Simplilearn
Experience 58 hours of applied learning and kickstart your machine learning journey. Dive into important topics like supervised learning, unsupervised learning, classification, regression, and time-series modeling with this comprehensive machine learning course. In addition to video lectures, you will get access to interactive labs, hands-on projects, and mentoring sessions from industry experts. The course is beginner-friendly and suitable for analytics managers, data scientists, machine learning engineers, and developers.
- Machine Learning Crash Course with TensorFlow APIs
Training Provider – Google
Go through a series of lessons with video lectures, hands-on practice exercises, and real-world case studies when you enroll in this course designed by Google Developers. With 15 hours of lectures, you will learn the basics of machine learning from Google researchers, work on over 30 exercises, and watch interactive visualizations of algorithms in action. You will learn how machine learning differs from traditional programming, gradient descent, how to measure loss, identify if an ML model is effective, how to represent data, and build a deep neural network.
- Machine Learning Course
Training Provider – Stanford University on Coursera
This 61 hours of in-depth training gives you a broad introduction to machine learning, statistical pattern recognition, and data mining. You will dive into a number of concepts like supervised learning, neural networks, support vector machines, clustering, dimensional reduction, bias theory, and more. Though you can audit the course for free, it is better to go for the paid version as you will get access to graded assessments and receive a course completion certificate at the end.
- Machine Learning with Python: A Practical Introduction
Training Provider – IBM on edX
Get all the tools required to start with supervised and unsupervised learning with this detailed course by IBM. Through this 5-weeks course (4 to 6 hours per week), you will explore the basics of machine learning using Python. You will understand how supervised learning differs from unsupervised learning and how statistical modeling is related to machine learning. Algorithms are regression, classification, dimensional reduction, and clustering are discussed thoroughly, including popular ML models like Random Forest, Root Mean Squared Error and Train/Test Split.
- Machine Learning Literacy
Training Provider – Pluralsight
Machine Learning Literacy is a learning path on Pluralsight which involves 5 courses and 35 hours of learning. The instructor will teach you the workflows, modeling techniques, and strategies behind any machine learning solution. They will explain how feature engineering fits into the ML workflow and how can one build their first features from numerical data. The program also covers various types of machine learning algorithms, and solution techniques based on the specifics of the problem you are trying to solve.
- AWS Machine Learning Engineer Nanodegree Program
Training Provider – Udacity
This 5-months training program on Udacity is the one-stop solution for gaining the job-ready skills of a machine learning engineer. Firstly, you will dive into the concepts of data science and machine learning and learn how to build and deploy ML models in production using Amazon SageMaker. When enrolling in this course, make sure you are already familiar with machine learning algorithms and Python programming. You will be using SageMaker to perform exploratory data analysis. Further, you will learn to create ML workflows, along with data cleaning and feature engineering.
- Introduction to Machine Learning and AI
Training Provider – Future Learn
Enroll in this short-term course if you want to understand the fundamentals of machine learning, how it works, and how to train your own AI. The program is designed by Raspberry Pi Foundation and will make you familiar with different types of machine learning. It also helps you understand the problems that machine learning can solve and the ethics of collecting data to train an ML model. Lastly, you will come across an important ML concept called neural networks (basically studied under deep learning).
- Machine Learning with Python
Training Provider – Cognitive Class by IBM
This beginner-level course is 3 hours long and helps you go through the foundations of machine learning using a popular programming language called Python. You will come to know about the real-life examples of machine learning and how it impacts society in ways you can’t think of. Learners will be given access to a hands-on lab for this course. The tool used will be Jupyter and you will be required to have a working knowledge of Python programming as applicable to data analytics.
- Machine Learning Scientist with Python
Training Provider – Datacamp
This is again a career track that involves 23 courses and 93 hours of learning modules. The course helps you understand how to perform supervised, unsupervised, and deep learning through Python programming skills. You will learn how to process data for features, train ML models, check performance, and tune parameters for higher accuracy. The program also covers natural language processing, image processing, and ML libraries like Spark and Keras.
- Complete Machine Learning and Data Science Bootcamp
Training Provider – Udemy
Do you want to learn data science and machine learning from scratch? If yes, then this course on Udemy is for you. This comprehensive training program will make you familiar with all the modern skills of a machine learning engineer and help you build many real-world projects to add to your portfolio. As a learner, you can access all the code, templates, and workbooks on GitHub. Some of the important topics covered are data exploration and visualization, neural networks, model evaluation, Python 3, Numpy, Scikit-Learn, and ML workflows.