Programmers and developers have greater access to data than ever before in history. For most applications, in fact, the sheer volume of data is so great that analysis is simply beyond what human beings are capable of. This is where machine learning comes in – the newest cool machine learning projects in the tech industry use groundbreaking technology to identity data patterns that human beings would likely overlook.

Education Prepares You for Machine Learning Projects

Machine learning is quickly becoming the go-to technology for making accurate predictions on large datasets. Even now, the most respected names in technology are using this technology to develop algorithms that can identify patterns in data without relying on constant human interaction. Predictive analytics, behavioral profile generation, and data mining are all useful applications that rely on machine learning.

But what does the budding machine learning programmer need to know before embarking on the journey from iterative supervised learning to an eventual true AI program? Succeeding in this exciting computer science discipline relies on gaining familiarity with a handful of valuable technologies and skillsets.

1. Python

While programming languages are by no means exclusive to certain fields of computer science, each one has its own strengths and weaknesses when it comes to achieving certain results. So far, Python is the best programming language for machine learning developers. A simple search in any tech job classifieds board will show that it is one of the most commonly desired skills for machine learning applications and positions.

That’s not to say that you cannot progress in the field of machine learning if you don’t know Python. Plenty of opportunities exist for programmers familiar with Java, C++, and R. But there is a good reason why the Google Brain Team chose Python for writing its cutting edge machine learning library TensorFlow. At the very least, you’ll need to develop familiarity with Python in order to follow the latest developments in the field.

2. Git and GitHub

Being familiar with GitHub is a must for any software developer working in a collaborative, innovative environment. The ability to host open-source code on a reliable version control repository platform is especially important when dealing with cool machine learning projects. This is because the very nature of machine learning makes effective analysis beyond the scope of a single human being – you’ll need to get used to collaborating efficiently.

Additionally, much leading coursework in the subject is hosted on GitHub repositories. Google’s Deep Learning course, for instance, assigns code to students through GitHub. You’ll have to be comfortable with the platform before you can start learning.

3. Statistics

Machine learning is dominated by statistics. The iterative algorithmic process used in reinforcement learning relies on statistical distribution models to describe machine feedback. As a machine learning program navigates its problem space – such as while driving a car or playing a video game – it uses statistic models to interact with the dynamic environment.

This means that you’ll have to gain at least intermediate knowledge of populations, samples, standard errors, variations, distributions, and more. Finding the means, medians, and modes of datasets will be of enormous importance in almost any machine learning project.

4. Calculus and Linear Algebra

Calculus and linger algebra are two more key mathematical concepts to succeeding in machine learning. Much of the legwork that goes into active developmental learning is based on concepts like derivatives, integrals, series expansions, and matrix operations through eigenvectors and eigenvalues. Without a masterful grasp of these concepts, even the simplest of cool machine learning projects may be outside your reach.

Fortunately, you can sign up for a free refresher course on these subjects with Udacity. This particular course is especially useful because it also involves Python – which you already know is a key skill in the world of machine learning.

Where to Take Courses for Cool Machine Learning Projects

Much what you need to know about machine learning is available online. Eric Jang, Research engineer at Google Brain recommends beginning with online courses.

In particular, Eric recommends Andrew Ng’s Machine Learning course on Coursera. This is the approach that Eric himself took in 2012. Despite having no previous experience with linear algebra or statistics, he was able to develop the necessary skills to eventually become a research engineer at one of the most prestigious tech companies in the field.

Another useful resource is Deep Learning, published by the MIT Press. This hardcover book offers a nuanced, three-part approach of a more academic nature than most online courses can offer. Ideally, any young developer interested in the subject should study materials both online and offline.

After gaining familiarity with the foundations of the subject, you can then specialize in any of the various subfields machine learning offers – generative models, Bayesian Deep Learning, computer vision, and meta-learning, for example.

With this background supporting you, you will have the knowledge and tools you need to take cool machine learning projects to the next level. Use these newfound skills to develop cutting edge algorithms that identify data patterns humans cannot, help with the budding field of home service robotics, or simply design a software program to play video games with – the choice is yours.

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