In today’s fast-paced environment, machine learning has become extremely important and essential in the technology industry.
Organizations around the world are looking for people who can harness the power of ML to solve their most important problems.
Additionally, if you are looking to earn enough to advance your career, machine learning courses can help you get a good chance.
Here are the top 10 machine learning courses that will give you a solid foundation in ML and lead you to a successful career in the field.
Machine Learning Andrew Ng (Coursera)
Coursera is one of the biggest ways to learn anything online for free. Andrew NG’s machine learning course on Coursera is his one of the most famous and rated ML courses available online.
Additionally, this course provides ML fundamentals including logistic regression, linear regression, neural networks, and other related fundamentals.
So the most important part is getting hands-on experience with real-world applications of ML, such as natural language processing and image recognition.
Applied Data Science with Python Specialization (Coursera)
Applied Science with a Specialization in Python is a comprehensive course offered online by the University of Michigan on Coursera.
The course offers a variety of topics including data manipulation, visualization, data analysis, and machine learning.
Additionally, it provides various tools and libraries used in Python for data science, such as Pandas, Numpy, and Matplotlib.
Introduction to machine learning with Python
This is a beginner’s course offered by IBM on Coursera. This course covers all the basics of ML including unsupervised or supervised learning, classification, regression and clustering.
However, the course also offers various ML models and techniques such as random forests, neural networks, and decision trees.
Machine Learning Engineer Nano Degree (Udacity)
The Machine Learning Engineer Nanodegree from Udacity is a course that covers all the fundamentals designed to provide you with a successful career.
Additionally, the course presents different stages of the ML lifecycle, including data preparation, model building, and deployment.
Deep Learning Specialization (Coursera)
This is a sub-field of ML focused on developing artificial neural networks.
This is a comprehensive course by Coursera that covers all the fundamentals of deep learning, including recurrent networks, convolutional networks, and generative models.
Applied Machine Learning (edX)
This is a special course on edX from Columbia University. The system offers a wide range of topics such as unsupervised and supervised learning, model selection, feature engineering, and evaluation.
In addition, the course also provides various ML models and techniques such as k-nearest neighbors, neural networks, and decision trees.
Data Science and Machine Learning Bootcamp (Udemy)
A comprehensive course powered by the robust online platform Udemy. The system offers a wide range of topics such as data cleaning, machine learning, data analysis and deep learning.
This course provides hands-on experience with popular ML tools and libraries such as TensorFlow and Scikit-Learn.
Applied Machine Learning by Kelleher and Tierney
The Applied Machine Learning course by Kelleher and Tierney focuses on the practical application of machine learning techniques to solve real-world problems.
Various topics such as data preparation, model selection, evaluation, and deployment are also covered.
Additionally, students will learn how to solve problems using various machine learning algorithms such as regression, clustering, classification, and recommender systems.
Machine Learning Crash Course (Google)
This is an introductory course that covers ML fundamentals, including focused engineering and model evaluation.
Advanced Machine Learning by Andrew Ng
It’s really for students who have a solid foundation in machine learning and want to go deeper into advanced topics.
Topics include deep learning, neural networks, natural language processing, and reinforcement learning.
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