Publisher: Rahul Kumar Sharma.

About this Course

In the course of the Machine Learning Specialization, you will get:

•  Build and train a neural network with TensorFlow to execute multi-class classification.
•  Apply best techniques for machine learning development so that your models generalize to details and tasks in the real world. 
• Build and use decision trees and tree getup methods, including incidental forests and boosted trees. 

The Machine Learning Specialization is a foundational online program developed in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these methods to build real-world AI applications. A leading AI visionary at Stanford University, Andrew Ng has led groundbreaking AI research at Google Brain, Baidu, and Landing.AI to advance the field of artificial intelligence. This 3-course Occupation is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million students since it launched in 2012. It provides an all-around introduction to modern machine learning, including supervised learning (multiple unbent regression, logistic degeneration, neural networks, and decision trees), unsupervised education (clustering, dimensionality decrease, recommender systems), and some of the best techniques used in Silicon Valley for artificial intelligence and machine learning invention (considering and tuning models, taking a data-centric technique to improve performance, and more.) By the end of this Occupation, you will have mastered key theoretical ideas and gained the practical know-how to quickly and deeply apply machine learning to challenging real-world problems. If you’re looking to break into AI or make a career in machine learning, the new Machine Learning Specialization is the best place to start.

WHAT YOU WILL LEARN

• Build and train a neural network with TensorFlow to perform multi-class categories.
• Apply most promising practices for machine learning development so that your models generalize to data and lessons in the real world
• Build and use finding trees and tree ensemble methods, including random forests and boosted trees.

Syllabus – What you will learn from this course after completing

WEEK 1:- Neural Networks

This week, you’ll learn about neural networks and how to operate them for various tasks. You’ll use the TensorFlow framework to create a neural network with just a few lines of code. Then, dive deeper by understanding how to code up your neural network in Python, “from scratch”. Optionally, you can understand more about how neural network analyses are implemented efficiently using parallel processing (vectorization).

WEEK 2:- Neural network training

This week, you’ll learn how to train your model in TensorFlow, also learn about further important activation processes (besides the sigmoid function), and where to use per type in a neural network. You’ll also learn how to go further from binary classification to multiclass classification (3 or more classes). Multiclass classification will teach you to a new activation function and a new loss function. Optionally, you can also understand the difference between multiclass classification and multi-label category. You’ll learn about the Adam optimizer, and why it’s an advancement upon regular gradient descent for neural network training. Finally, you will get a brief opening to other layer types besides the one you’ve seen thus far.

WEEK 3:- Advice for applying machine learning

This week you’ll learn the best methods for training and evaluating your learning algorithms to improve performance. This will protect a wide range of useful advice about the machine learning lifecycle, tuning your model, and also improving your activity data.

WEEK 4:- Decision trees

This week, you’ll learn about a practical and very typically used learning algorithm the decision tree. You’ll also learn about variations of the decision tree, including unexpected forests and boosted trees (XGBoost).

About the Machine Learning Specialization

The Machine Learning Specialization is a foundational online program developed in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly schedule will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.

Among Andrew Ng’s accomplishments are the leading AI research at Stanford University, his groundbreaking work at Baidu, Google Brain, and Landing.AI that has advanced the field of AI. This 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it was established in 2012. It delivers a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial brilliance and machine learning innovation (evaluating and tuning models, taking a data-centric technique to improve performance, and more.) By the end of this Specialization, you will have mastered key ideas and gained the practical know-how to quickly and powerfully apply machine learning to questioning real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start your Machine Learning journey.

Ways to take this course

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