It's never been easier to get started with machine learning. In addition to structured massive open online courses (MOOCs), there are a huge number of incredible, free resources available around the web. Here are a few that have helped me. Familiarity and moderate expertise in at least one high-level programming language is useful for beginners in machine learning. Unless you are a Ph.D. researcher working on a purely theoretical proof of some complex algorithm, you are expected to mostly use the existing machine learning algorithms and apply them in solving novel problems.
Today, Python has become one of the most favored programming languages among developers across the globe – from process automation to scripting to web development to machine learning – it's used everywhere. Before we delve deeper to understand why Python is steadily becoming a great choice among machine learning professionals, let's have a quick look at where actually the study of algorithms helps in. Perhaps you already know that artificial intelligence (AI) stands for any intelligence demonstrated by a machine in order to obtain an optimal solution. Machine learning, which is a part of the broad category of data science, is what takes the solution further by using algorithms that finally helps in making informed decisions. In the context of information technology, we can see that companies are increasingly investing strategically into resource pools associated with machine learning.
In the ever-changing ecosystem of data science tools, you often find yourself needing to learn a new language in order to keep up with the newest methods or to more effectively collaborate with coworkers. I've been an R coder for a few years, but wanted to transition to Python in order to take full advantage of the deep learning libraries and tools such as PySpark. Also, I joined the data science team at Zynga, where Python is the preferred language. It's only been a few weeks, but I'm starting to get the hang of performing exploratory data analysis and predictive modeling in this new language. This isn't the first time that I've tried to quickly ramp up on a new data science language, but it has been the most successful.