In today’s fast-paced environment, machine learning has become very important and essential in the tech industry.Organizations worldwide are searching for individuals who can harness the power of ML to solve critical issues.
Moreover, If you are willing to earn well, and advance your career, then the Machine Learning course will help you get good opportunities.
Here we are bringing the top 10 Machine Learning Courses that will provide you with a solid foundation in ML and will give you a successful career in this field.
Machine Learning By Andrew Ng (Coursera)
Coursera is one of the biggest to learn anything free of cost online. Andrew NG’s machine learning course on Coursera is one of the most famous and highly rated ML courses available online.
In addition, the course provides the basics of ML, including logistic regression, linear regression, neural networks and other related basics.
Hence, the most significant part is, it provides 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 Python specialization is a comprehensive course offered by the University of Michigan online on Coursera.
The course offers various topics, including data manipulation, visualization, data analysis and machine learning.
Moreover, it offers different tools and libraries used in Python for data science, such as Pandas, Numpy and Matplotlib.
Introduction To Machine Learning With Python
It is a beginner-friendly course offered by IBM on Coursera. The course covers all the fundamentals of ML, including unsupervised or supervised learning, classification, regression, and clustering.
However, the course also offers different ML models and techniques, such as random forests, neural networks and decision trees.
Machine Learning Engineer Nanodegree (Udacity)
The Machine Learning Engineer Nanodegree offered by Udacity is a course that covers all the fundamentals designed to provide you with a successful career.
Moreover, the course offers different stages of an ML life cycle, including data preparation, model building and deployment.
Deep Learning Specialization (Coursera)
It is a subfield of ML that focuses on developing artificial neural networks.
It is a comprehensive course by Coursera that covers all the foundations of deep learning, including recurrent networks, convolutional networks, and generative models.
Applied Machine Learning (edX)
It is a course specifically by Columbia University on edX. The system offers a range of topics, including unsupervised and supervised learning, model selection, feature engineering and evaluation.
In addition, the course also offers different ML models and techniques, including k-nearest neighbours, neural networks and decision trees.
Data Science And Machine Learning Bootcamp (Udemy)
It is a comprehensive course by the robust online platform Udemy. The system offers a range of topics, including data cleaning, machine learning, data analysis and deep learning.
The course offers hands-on experience with popular ML tools and libraries such as TensorFlow and Scikit-Learn.
Applied Machine Learning by Kelleher And Tierney
The course Applied Machine Learning by Kelleher and Tierney focuses on the practical application of machine learning techniques to solve real-world problems.
It also covers various topics, including data preparation, model selection, evaluation and deployment.
Moreover, students will learn to use various machine-learning algorithms to solve problems, including regression, clustering, classification and recommendation systems.
Machine Learning Crash Course (Google)
It is a beginner-friendly course that covers the basics of ML, including featured engineering and model evaluation.
Advanced Machine Learning by Andrew Ng
It is precisely for students who have a solid understanding of the basics of machine learning and want to get deeper into advanced topics.
The topics include deep learning, neural networks, natural language processing and reinforcement learning.
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