Hii, looking for best Machine Learning Courses Online and got confused ?
Don’t worry, here you’ll see top 6 hand picked Machine Learning Courses Online from Various Top Online Course provide like Coursera, udemy, edx, etc.
What is Machine Learning ?
Machine learning is an application of articial intelligence (AI) that provides systems the ability to automatically learn an improve from experience without being explicitly programmed. Machine learning focuses on the development of comput programs that can access data and use it learn for themselves.
Here is a small intro to Machine Learning.
Best Machine Learning Courses Online
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition.
Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
This course dives into the basics of machine learning using an approachable, and well-known programming language, Python.
In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.
In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed!
By just putting in a few hours a week for the next few weeks, this is what you’ll get. 1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy 2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more. 3) And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media.
Visit Coursera Offered by National Research University Higher School of Economics
You’ll learn the models and methods and apply them to real world situations ranging from identifying trending news topics, to building recommendation engines, ranking sports teams and plotting the path of movie zombies.
Major perspectives covered include: probabilistic versus non-probabilistic modeling supervised versus unsupervised learning
Topics include: classification and regression, clustering methods, sequential models, matrix factorization, topic modeling and model selection.
Methods include: linear and logistic regression, support vector machines, tree classifiers, boosting, maximum likelihood and MAP inference, EM algorithm, hidden Markov models, Kalman filters, k-means, Gaussian mixture models, among others.
Learn the most important machine learning models, including how to create them yourself from scratch, as well as key skills in data preparation, model validation, and building data products.
There are around 24 hours of lessons, and you should plan to spend around 8 hours a week for 12 weeks to complete the material. The course is based on lessons recorded at the University of San Francisco for the Masters of Science in Data Science program.