Learn Machine Learning
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 automatically learn and improve from experience without being explicitly programmed.
The process of learning 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 aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly. The workflow of the machine learning process has been depicted in the pictorial form as below.
About this Course
In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI.
In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only 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.
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 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.
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. Also, this course is for free, so go ahead and get yourself enroll, please don’t forget to rate this course in our reviews section.
Syllabus – What you will learn from this course
(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:
- This module in the week first of the learn machine learning course 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 the notion of a cost function, and introduce the gradient descent method for learning.
3) Linear Algebra Review
- This optional module in the learn machine learning course which is a refresher on linear algebra concepts.
- Basic understanding of linear algebra is necessary for the rest of the course, especially 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.
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.
- This module in the third week of the learn machine learning course introduces the notion of classification, the cost function for logistic regression, and the application of logistic regression to multi-class classification.
- 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.
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, it is likely that a neural network is helping to understand your speech; when you cash a check, the machines that automatically read the digits also use neural networks.
8) Neural Networks: Learning
- This module in the fifth week of the learn machine learning course introduces to the backpropagation algorithm that is 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.
9) Advice for Applying Machine Learning
Applying machine learning in practice is not always straightforward.
- In this module, best practices for applying machine learning in practice have been shared
- 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 first understand where the biggest improvements can be made.
- This module explains how to understand the performance of a machine learning system with multiple parts, and also how to deal with skewed data.
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.
12) Unsupervised Learning
- Unsupervised learning will be used to build models that help us understand our data better.
- k-Means algorithm for clustering will be discussed in order 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 as well as for visualizations of complex datasets.
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 can be 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.
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.
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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- A complete and outstanding summary of main learning algorithms.
- Precise presentation of mathematical and statistic concepts behind each algorithm.
- Python has not been included
- The quizzes are very basic
Specification: Learn Machine Learning (Andew Ng’s FREE Course)