Sci-fi movies and shows have allowed us to create some unrealistic expectations when it comes to technology. In the early 90s, we were thinking about flying cars and hover boards, and even transportation machines. Fast-forward to 2018, we are still no where closer to the brilliant technologies that we thought up then, but we do some pretty cool tech today all thanks to Machine Learning and Artificial Intelligence.
From autonomous cars to self-starting coffee machines, the world is rapidly changing. Technology is progressing at an even faster rate, where we have now advanced from 3D to Virtual Reality and chatbots to actual AI robots. It isn’t that far off when we might actually have robot servants to self-driving cars that will make your lives more comfortable and easy.
When we think of these advancements, we cannot overlook one major technological component that has made all of this possible – Machine Learning.
What is Machine Learning?
Machine Learning is a branch of science that deals helping computers learn. Using a set of cases and scenarios, machines can learn from previous decisions made and then make them. The lifeline for Machine Learning is data, which allows computers to plot every possible outcome and then proceed to provide information based on that.
There are two types of Machine Learning – Unsupervised and Supervised.
In supervised learning, a programmer provides the computers with inputs and a desired output, which the computer must then figure out.
In Unsupervised learning, the programming will only provide the computer with an input, leaving it to find out it’s own structure and output.
History of Machine Learning
The concept of machine learning can be dated back to Alan Turing and his Turing Test, which was used to determine if computers had real intelligence. However, the term ‘machine learning’ was originally coined in 1952 by Arthur Samuel while he was at IBM. Samuel was responsible for writing the algorithm for a game of Checkers that improved with each round that it played.
The idea of machine learning goes back before that, but only after did Samuel coin the term did they have a proper name to give for helping computers learn from data or data sets. From then on, Machine Learning has progressed to the point where we use it daily in our lives – from GPS to even your fancy coffee machines.
Benefits of Machine Learning
There are many different benefits of machine learning, and the benefits can exemplify based on the industry that it is being used in.
- Image & Voice Recognition – Voice is playing a bigger part with each innovation, where everyone wants their technology to be hands-free. Simply speak to your device and you want it to speak back to you. This is exactly what’s happening with Siri, Amazon Echo, Google and many others. Machine Learning allows the algorithms to improve which results in lesser mistakes and a higher recognition level.
- Advanced Customization – Anyone who has ever used Google understands how Google suggestions work, which guesses we might want to search even before we search that term. Similarly, Netflix’s system offers recommendations for other shows and movies, based on the previous ones you might have seen.
- Intelligent data analysis – Data plays an important role but as the number of users on the internet grow, so does the data that is generated from them. To analyze and find patterns in this data takes time and energy, but with machine learning you can drastically reduce the time. Simply plug in the right algorithm and the machine will do the work for you.
- Sensory data analysis – If you’ve used any smart monitors you know that they are capable of tracking steps, heart rate and activities. But, sometimes it gets the information wrong, or you might have to specifically add an activity to the list. Well, machine learning even changes that. With more data and improved features, the app or device will automatically be able to change the activity or the surrounding condition.
Eduonix Machine Learning Course on Kickstarter
Since, Machine Learning is now bigger than ever – it is now when we need more people in this rat race! But Machine Learning is time consuming and expensive, this is why Eduonix has introduced this amazing course. To help developers who want a better chance to get into Machine Learning without having to spend thousands of dollars, Eduonix’s course brings Machine Learning to your homes at an exceptionally cheaper cost.
The course offers a hands-on experience with Machine Learning, where developers will get a chance to actually work along with the instructor to build 5 different projects from scratch. Each project ranges from easy to more advanced as you progress on.
Benefits of This Course
Eduonix has designed the syllabus from the ground up, keeping in mind the newbies and has simplified the process of teaching. The course bridges the gap between theory and the real world, by bringing you bits and pieces to prepare. The projects used in this tutorial includes real world projects that you actually use to build and hone your skills.
Other courses are expensive and includes a confusing system that leaves you confused at the end, but this course places a focus to actually prepare you for the real world and leaves you with not only hands-on experience but also actual projects that you can use on your resume.
- Project 1 – Stock Market Clustering Project
This project will focus on using K-means clustering algorithm to identify related companies by finding correlations among stock market movements over a given time span. It also includes learning the Yahoo Finance Python module and how to do a PCA dimensionality reduction to plot the data on a 2D plot.
- Project 2 – Breast Cancer Detection
In the second project, your program will help detect breast cancer malignancies by using a support vector machine. You will also learn how to use the K-nearest neighbor algorithm to compare and contrast performance with the support vector machine.
- Project 3 – Board Game Review
In this project you are going to predict the average reviews on a board game based on characteristics such as difficulty, length, number of players by performing linear regression analysis.
- Project 4 – Credit Card Fraud Detection
In this project, you are going to do a credit card fraud detection and going to focus on anomaly detection by using probability densities.
- Project 5 – Diabetes Onset Detection
Combining your knowledge from other projects, in this final project you will learn how to build a deep learning grid search. You will also learn how to fine-tune a deep learning neural network by performing a grid search, which will use the network to detect the onset of diabetes based on patient data.
With the groundwork already done and the syllabus finalized, Eduonix still needs a lot of help to make this course best for Machine Learning. With your support and pledges, you can help make this course a reality. All you need to do is go to Eduonix’s Kickstarter Campaign Page and pledge!