Top Online Machine Learning Courses for Beginners In 2021 – Analytics Insight

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Machine Learning
Machine learning is one of the thriving and fast-paced domains in the fields of computer science as well as technology. Multiple industries and companies have started leveraging machine learning to boost productivity and transform the existing workforce into a more efficient and intelligent workforce with effective human-machine interaction. Students or working professionals are highly interested to work with AI/ML algorithms in reputed companies across the world. Completing machine learning courses provides confidence and a strong understanding of concepts and mechanisms with a valid and valuable certificate. Let’s explore some of the top online machine learning courses for beginners in 2021 that can add value to the CV and professional career.
 
 
Mathematics for Machine Learning Specialization from Coursera is one of the top machine learning courses for beginners in 2021 with the enrolment date starting from October 26, 2021. Imperial College, London offers this online course for beginners to help them implement mathematical concepts with real-world data and understand how orthogonal projections work. The online machine learning course covers the usage of linear algebra to calculate the page rank of a small simulated internet, performing a non-linear least squares regression to fit an ML model to a dataset and many more. It will take four months for a beginner to complete the machine learning course with a certificate upon completion. Beginners can also gain hands-on experience with practical projects. The machine learning specialization includes three courses— linear algebra, multivariate calculus, and PCA (Principal Component Analysis).
 
Machine Learning A-Z: Hands-on Python & R In Data Science from udemy is one of the online machine learning courses for beginners to create machine learning algorithms in Python and R, ML models, NLP, and many more. The online course for beginners consists of 45 sections and 320 lectures that include data pre-processing, regression, classification, clustering, association rule learning, reinforcement learning, NLP, model selection and boosting, and dimensionality reduction. Beginners should have the basic knowledge of high school mathematics level to complete this machine learning course and earn a certificate in the end. It also consists of Python and R templates for downloading as well as hands-on practice on building individual ML models.
 
Machine Learning Crash Course from Google with TensorFlow APIs is one of the top online machine learning courses for beginners in 2021. It features a series of machine learning lessons with multiple video lectures, real-world case studies, as well as hands-on practical experience that include more than 30 exercises, 25 lessons, and interactive visualizations of ML algorithms in action. Beginners should prepare to work with large datasets for solving different ML problems in models.
 
Machine Learning from edX is a popular online course for beginners with 8-10 hours per week for 12 weeks. Beginners can learn about ML models, methods, and applications in a real-world environment. This online machine learning course covers probabilistic v/s non-probabilistic modeling as well as supervised v/s unsupervised learning with different topics like classification, regression, clustering, matrix factorization, and many more. Prerequisites of this machine learning course include calculus, linear algebra, probability, coding, and comfort with data manipulation.
 
ML for Beginners from Microsoft offers a 12-week with 26 lessons about machine learning in this online course. Beginners can learn about classic machine learning with Scikit-learn as an ML library. Each lesson consists of pre and post-lesson quizzes, written instructions, assignments, new project creations, optional sketch notes, and many more. The curriculum of this online course for beginners covers the introduction to machine learning, the history of machine learning, the introduction to regression, classification, clustering, and many more.
 

 
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