Learn Machine Learning (Andew Ng’s FREE Course)

Coursera

Category: Data Science Tags: All levels, Online Course

Learn Machine Learning from Stanford

Machine learning is the science of getting computers to act without being explicitly programmed. In this course, you will learn Machine Learning with the most effective techniques, gain practice implementing them, and getting them to work for yourself. More importantly, you’ll learn not only about the theoretical underpinnings of learning but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI.

About Machine Learning

Machine learning is an application of artificial intelligence (AI) that consists of various learning methods (Supervised, unsupervised, semi-supervised & Reinforcement learning). The machine learning algorithms provides systems the ability to learn and improve from experience without being explicitly programmed automatically.
The learning process begins with observations or data, such as examples, direct experience, or instruction, to look for patterns in data and make better decisions in the future based on the examples that will be provided. The primary goal is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly. The workflow of the machine learning process is depicted in the pictorial form below.

Why Machine Learning Matters?

With the rise in big data, machine learning has become an essential technique for solving problems in areas, such as:

• Computational finance for credit scoring and algorithmic trading.
• Image processing and computer vision for face recognition, motion detection, and object detection.
• Computational biology for tumor detection, drug discovery, and DNA sequencing.
• Energy production, for price and load forecasting.
• Automotive, aerospace, and manufacturing for predictive maintenance.
• Natural language processing for voice recognition applications.

“Machine learning will bring about not just a new era of civilization, but a new stage in the evolution of life on earth.”

― Pedro Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

Skills you will gain from this course?

The course will help you to draw from numerous case studies and applications. You’ll also learn how to apply learning algorithms and to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas. You will get the following skills from this course:

• Logistic Regression.
• Artificial Neural Network.
• Machine Learning and Machine Learning (ML) Algorithms.

About this Course

This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Content included as below:

• Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
• Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
• 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. Thus, you will 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.

About the tutor of this Course

Recently developed Google technology called Google Brain was formally founded by the Google team led by Andrew Ng, who is the tutor of this course on Coursera and Chief Scientist at Baidu Research that eventually resulted in the productization of deep learning technologies across a large number of Google services.

Andrew Ng has spoken and written a lot about what deep learning is and is a good place to start. I am sure you would get more than you expect if enrolled in this course taught by Andrew Ng. Moreover, this full course you will get for free (but have to pay for a certificate; financial aid also available), so go ahead and get yourself enroll, please don’t forget to rate this course in our reviews section.

Syllabus

(Overall content rating 97%)

You can learn machine learning free of cost in this course organized by Stanford University. The course contains content for 11 weeks as follows:

Week 1: Learn Machine Learning

1) Introduction

• In week first of the learn machine learning course, this module introduces you to the core idea of teaching a computer to learn concepts using data—without being explicitly programmed.
• Will be introducing to Supervised and Unsupervised learning

2) Linear Regression with One Variable

• Linear regression predicts a real-valued output based on an input value.
• The application of linear regression to housing price prediction will be discussed with a cost function and introduce the gradient descent method for learning.

Week 2: Learn Machine Learning

3) Linear Algebra Review

• This optional module in the learn machine learning course is a refresher on linear algebra concepts.
• A basic understanding of linear algebra is necessary for the rest of the course, mostly to cover models with multiple variables.

4) Octave/Matlab Tutorial

• This course includes programming assignments designed to help users understand how to implement the learning algorithms in practice.
• To complete the programming assignments, you will need to use Octave or MATLAB.
• This module introduces Octave/Matlab and shows you how to submit an assignment.

Week 3: Learn Machine Learning

5) Logistic Regression

Logistic regression is a method for classifying data into discrete outcomes. For example, we might use logistic regression to classify an email as spam or not spam.

• In the third week of the learn machine learning course, this module introduces the notion of classification, the cost function for logistic regression, and the application of logistic regression to multi-class classification.

6) Regularization

• Machine learning models need to generalize well to new examples that the model has not seen in practice.
• This module introduces regularization, which helps prevent models from overfitting the training data.

Week 4: Learn Machine Learning

7) Neural Networks: Representation

The neural network is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understands your voice commands, a neural network is likely helping to understand your speech. When you cash a check, the machines that automatically read the digits also use neural networks.

Week 5: Learn Machine Learning

8) Neural Networks: Learning

• This module in the fifth week of the learn machine learning course introduces the backpropagation algorithm used to help learn parameters for a neural network.
• At the end of this module, you will be implementing your own neural network for digit recognition.

Week 6: Learn Machine Learning

9) Advice for Applying Machine Learning

Applying machine learning in practice is not always straightforward.

• In this module, you will learn best practices for applying machine learning.
• Also have discussed the best ways to evaluate the performance of the learned models.

10) Machine Learning System Design

To optimize a machine learning algorithm, you’ll need to recognize where significant improvements are required?

• This module explains how to understand a machine learning system’s performance with multiple parts and how to deal with skewed data.

Week 7: Learn Machine Learning

11) Support Vector Machines

• Support vector machines, or SVMs, is a machine learning algorithm for classification.
• This module will introduce the idea and intuitions behind SVMs and discuss how to use them in practice.

Week 8: Learn Machine Learning

12) Unsupervised Learning

• Use unsupervised learning methods to build models that help us understand our data better.
• k-Means algorithm for clustering will be discussed to learn groupings of unlabeled data points.

13) Dimensionality Reduction

• This module in the eighth week of the learn machine learning course introduces you to the Principal Components Analysis and shows how it can be used for data compression to speed up learning algorithms and visualize complex datasets.

Week 9: Learn Machine Learning

14) Anomaly Detection

Given a large number of data points, we may sometimes want to figure out which ones vary significantly from the average. For example, in manufacturing, we may want to detect defects or anomalies.

• This module shows how a dataset are modeled using a Gaussian distribution and how the model can be used for anomaly detection.

15) Recommender Systems

When you buy a product online, most websites automatically recommend other products that you may like. Recommender systems look at patterns of activities between different users and different products to produce these recommendations.

• This module will be introducing recommender algorithms such as the collaborative filtering algorithm and low-rank matrix factorization.

Week 10: Learn Machine Learning

16) Large Scale Machine Learning

Machine learning works best when there is an abundance of data to leverage for training.

• This module will discuss how to apply machine learning algorithms with large datasets.

Week 11: Learn Machine Learning

17) Application Example: Photo OCR

Identifying and recognizing objects, words, and digits in an image is a challenging task.

• In this module, at the end of the ‘learn machine learning course’, we will discuss how a pipeline can be built to tackle this problem and how to analyze and improve the performance of such a system.

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