Supervised Machine Studying: Regression and Classification

About this Course

Within the first course of the Machine Studying Specialization, you’ll:

• Construct machine studying fashions in Python utilizing widespread machine studying libraries NumPy and scikit-learn. • Construct and practice supervised machine studying fashions for prediction and binary classification duties, together with linear regression and logistic regression The Machine Studying Specialization is a foundational on-line program created in collaboration between DeepLearning.AI and Stanford On-line. On this beginner-friendly program, you’ll be taught the basics of machine studying and the best way to use these strategies to construct real-world AI purposes. This Specialization is taught by Andrew Ng, an AI visionary who has led essential analysis at Stanford College and groundbreaking work at Google Mind, Baidu, and Touchdown.AI to advance the AI discipline. This 3-course Specialization is an up to date and expanded model of Andrew’s pioneering Machine Studying course, rated 4.9 out of 5 and brought by over 4.8 million learners because it launched in 2012. It offers a broad introduction to fashionable machine studying, together with supervised studying (a number of linear regression, logistic regression, neural networks, and choice timber), unsupervised studying (clustering, dimensionality discount, recommender methods), and among the finest practices utilized in Silicon Valley for synthetic intelligence and machine studying innovation (evaluating and tuning fashions, taking a data-centric method to bettering efficiency, and extra.) By the tip of this Specialization, you’ll have mastered key ideas and gained the sensible know-how to shortly and powerfully apply machine studying to difficult real-world issues. In the event you’re seeking to break into AI or construct a profession in machine studying, the brand new Machine Studying Specialization is the perfect place to start out.

WHAT YOU WILL LEARN

• Construct machine studying fashions in Python utilizing widespread machine studying libraries NumPy & scikit-learn

• Construct & practice supervised machine studying fashions for prediction & binary classification duties, together with linear regression & logistic regression

SKILLS YOU WILL GAIN

• Regularization to Keep away from Overfitting
• Gradient Descent
• Supervised Studying
• Linear Regression
• Logistic Regression for Classification

Syllabus – What you’ll be taught from this course

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WEEK

1

7 hours to finish

Week 1: Introduction to Machine Studying

Welcome to the Machine Studying Specialization! You’re becoming a member of hundreds of thousands of others who’ve taken both this or the unique course, which led to the founding of Coursera, and has helped hundreds of thousands of different learners, such as you, check out the thrilling world of machine studying!

WEEK

2

10 hours to finish

Week 2: Regression with a number of enter variables

This week, you’ll lengthen linear regression to deal with a number of enter options. You’ll additionally be taught some strategies for bettering your mannequin’s coaching and efficiency, corresponding to vectorization, characteristic scaling, characteristic engineering and polynomial regression. On the finish of the week, you’ll get to apply implementing linear regression in code.

WEEK

3

16 hours to finish

Week 3: Classification

This week, you’ll be taught the opposite sort of supervised studying, classification. You’ll discover ways to predict classes utilizing the logistic regression mannequin. You’ll find out about the issue of overfitting, and the best way to deal with this drawback with a technique known as regularization. You’ll get to apply implementing logistic regression with regularization on the finish of this week!