Machine Learning by Stanford University (Coursera)

This Stanford Machine Learning Certification has been created by Andrew Ng, the most renowned expert in AI and Machine Learning, cofounder of Coursera, founding lead of Google’s deep learning research unit Google Brain, former head of AI at Baidu, and currently CEO at Landing AI. It is an updated version of Andrew’s most popular Machine Learning course, that was taken by over 4.8 million learners on Coursera.

The popularity of this new, updated foundational program in AI and Machine Learning can be gauged from the fact that around 50K students and professionals signed up for the program in the first few weeks of its launch and 95% of them have given it a 5-star rating. Undoubtedly, AI experts cite this program as the single most important resource for anyone looking to learn AI and ML.

This is a three course specialization that introduces learners to the core ideas of AI, machine learning, datamining and statistical pattern recognition. It imparts them a good grounding in the mathematical, statistical, and computer science fundamentals that form the basis of automated learning machines. The course material is very extensive and requires around 3 months to complete with about 9-10 hours of effort per week. It covers the following topics:

• Difference between supervised and unsupervised learning and regression and classification tasks
• Build and train linear regression models
• Implement and understand the purpose of a cost function
• Methods for improving machine learning models by choosing the learning rate, plotting the learning curve, performing feature engineering, and applying polynomial regression
• Logistic regression model for classification
• Build and train a neural network with TensorFlow
• Build and use decision trees and tree ensemble methods, including random forests and boosted trees
• Applying machine learning algorithms with large datasets
• Performance of a machine learning system with multiple parts
• Best practices for applying machine learning
• Use unsupervised learning techniques
• Build recommender systems with a collaborative filtering approach and a content-based deep learning method
• Build a deep reinforcement learning model

For programming assignments, the courses use Python, which is a simple way to learn the fundamentals of ML.

Numerous case studies and applications are included in the program to help learners get hands-on practice. They get 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.

Key Highlights

• Highest rated amongst the top free Machine Learning and AI courses available online
• Excellent fit for beginners in the field of artificial intelligence and machine learning
• Learn about the most effective machine learning techniques, and gain practice implementing them
• Learn about some of Silicon Valley’s best practices in the field of Machine Learning and AI innovation
• Gain the practical know-how needed to quickly and powerfully apply ML techniques to new real life situations and problems
• Study the courses for free; option to get a paid certificate for showcasing your learning of AI and ML skills

Duration : Approx 3 months, 9 hours per week

Sign up Here