Learn Machine Learning With This Free Course


Machine learning is a branch of artificial intelligence. It is the study of algorithms that can learn from data and then use that information to make predictions or decisions.

Machine learning entails a number of terms, many which are quite technical. To simplify machine learning, it can be broken down into three stages: training, inference, and deployment. Training is the process of providing machine-learning algorithms with large datasets to learn from and teach themselves how to make predictions about new things they have not seen before. Inference is applying those learned skills in order to make decisions based on new data they have never seen before. Finally, deployment deals with putting those skills into practical use by applying them to real-world situations where they will be used day-to-day.

Machine learning is used in many different fields such as natural language processing, image recognition, and voice recognition. Its applications range from search engines, spam filters, and computer vision to biotech and medical applications. It’s a very powerful tool that can be used for a variety of objectives.

To be a machine learning expert, you need to learn math, statistics, probability theory, and linear algebra.

You also need to have knowledge of artificial intelligence concepts like deep learning and neural networks.

And finally, you should know about the latest developments in the machine learning field.

Machine Learning is one of the most important technologies for the future. It offers the opportunity to build smarter products, services and robots. It uses algorithms that can learn from data. There are plenty of courses available on the internet that teach you machine learning in a variety of ways.

If you are just starting to learn and looking for a comfortable start in your learning process, we  recommend you enroll with this Free Course thats offered by Stanford University through the Coursera Platform. The Course is taught by Andrew Ng, the founder of DeepLearning.AI and Coursera Co-founder. A highly reputable instructor and expert in the field of machine learning. 

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include:

1) Supervised learning(parametric/non-parametric algorithms, support vector machines, kernels, neural networks ).

(ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).

(iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

The course also contains numerous case studies and applications, so you will get to learn how to apply learning algorithms such as in building robots with perception and control, web searches and with understanding text. There is also a lot of content on developing computer visions for medical applications. 

In summary, this is what you will learn in this course.

1) Linear regression

Linear Regression is a statistical technique used to draw linear relationships between two variables. This technique is best applied in cases where the relationship is linear and also when the two variables are highly correlated.

2) Linear Algebra

Linear Algebra is a mathematical system that deals with geometrical problems of two dimensions and more. It studies the operations and laws of linear equations and their solutions. You need to master the fundamentals of linear algebra in machine learning. This is a course that teaches everything you need. Add this to your tools and you journey to become machine learning expert will be smooth.

3) Octave/Matlab Tutorial 

The learning and assignment in this course will cover coding in Octave/MATLAB. To complete the program, you will need to use Octave or MATLAB. This module teaches you octave/Matlab and gives you familiarity on submitting your assignments. 

4) Logistic regression

Logistic regression is a statistical technique used to determine the relationship between an independent and dependent variable, such as the likelihood of a person’s susceptibility to a disease. It is often used for predicting categorical variables with two categories such as running or not running. In this module, you will be introduced  the notion of classification, the cost function for logistic regression and the application of logistic regression to multi-class classification.

Other machine learning modules covered in this course include the following.

·         Neural Networks

·         Advice for applying machine learning

·         Support vector machines

·         Unsupervised learning

·         Dimensionality reduction

·         Anomaly detection

·         Recommender systems

·         Large scale machine learning

·         Application example: Photo OCR

 Now that we have some of the thing covered in this course, Let us give you some advantages of why you should take this training.

·         Flexible deadlines

You can always reset the deadlines t fit in your own schedules

·         Get a certificate.

This may or may not be free but you will get a certificate after completion. Coursera has a financial aid and you can apply to get the certificate and incase you require.

·         100% online

Learn at your own pace from the comfort of your home/office.

Enroll The Course Now From Here




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