As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine Learning applications are everywhere, from self-driving cars to spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this course, you'll be introduced to the Natural Processing Language and Recommendation Systems, which help you run multiple algorithms simultaneously.
As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine Learning applications are everywhere, from self-driving cars, spam detection, document searches, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of big data and data science. The main challenge is how to transform data into actionable knowledge. In this course you will learn all the important Machine Learning algorithms that are commonly used in the field of data science.
There is no shortage of articles attempting to lay out a step-by-step process of how to become a data scientist. Are you a recent graduate? Do this… Are you changing careers? Do that… And make sure you're focusing on the top skills: coding, statistics, machine learning, storytelling, databases, big data… Need resources? Check out Andrew Ng's Coursera ML course, …". Although these are important things to consider once you have made up your mind to pursue a career in data science, I hope to answer the question that should come before all of this. It's the question that should be on every aspiring data scientist's mind: "should I become a data scientist?" This question addresses the why before you try to answer the how. What is it about the field that draws you in and will keep you in it and excited for years to come? In order to answer this question, it's important to understand how we got here and where we are headed. Because by having a full picture of the data science landscape, you can determine whether data science makes sense for you. Before the convergence of computer science, data technology, visualization, mathematics, and statistics into what we call data science today, these fields existed in siloes -- independently laying the groundwork for the tools and products we are now able to develop, things like: Oculus, Google Home, Amazon Alexa, self-driving cars, recommendation engines, etc. The foundational ideas have been around for decades... early scientists dating back to the pre-1800s, coming from wide range of backgrounds, worked on developing our first computers, calculus, probability theory, and algorithms like: CNNs, reinforcement learning, least squares regression. With the explosion in data and computational power, we are able to resurrect these decade old ideas and apply them to real-world problems. In 2009 and 2012, articles were published by McKinsey and the Harvard Business Review, hyping up the role of the data scientist, showing how they were revolutionizing the way businesses are operating and how they would be critical to future business success. They not only saw the advantage of a data-driven approach, but also the importance of utilizing predictive analytics into the future in order to remain competitive and relevant. Around the same time in 2011, Andrew Ng came out with a free online course on machine learning, and the curse of AI FOMO (fear of missing out) kicked in. Companies began the search for highly skilled individuals to help them collect, store, visualize and make sense of all their data. "You want the title and the high pay?