Big Data and Machine Learning are among the most talked-about concepts in the world of tech today. However, the average person would seriously struggle to actually define what they mean or what a Machine Learning course would entail.
At Ubiqum Code Academy, we have a specialized course in Machine Learning and Data Analytics, and in this short article, we’ll tell you what Machine Learning is and how it is used in data analysis.
One of the common definitions goes something along the lines of: “Machine learning, or automated learning, is a branch of artificial intelligence that allows machines to “learn” from data analysis to identify patterns and support decision making with minimal human intervention”.
At Ubiqum, we think this definition is rather abstract and inaccurate.
Firstly, because machines do not “learn”. They are lifeless instruments that execute very complex calculations very fast, but everything they execute has been programmed by humans. While machines now can compile code and make minor adjustments, we’re still far from a computer that “self-programs” itself, in the sense that it consciously identifies issues and alters the code of its programs according to its own experience, something that the human mind and memory do.
In any case, what is certain is that software engineering, together with the power of today’s computers and the enormous amount of digital data available have given rise to complex programs that allow us to process and analyze a huge amount of data efficiently. This method was out of reach for even the top tech companies just a couple of decades ago but nowadays, it has utterly revolutionized the way we do business.
So, to help decipher how things are today, we’ll start by describing the three general profiles that outline Machine Learning, Big Data, and Data Analytics:
1. Data Scientist
First of all, we need people who develop algorithms. These algorithms are very complex software programs and require a great deal of specialization. A Data Scientist, as described here, is a software engineer on steroids, so it’s no wonder they have about 10 to 15 years of work experience and possibly a PhD that focuses on new Machine Learning algorithms.
2. Data Engineering
Secondly, we need people who deal with the data collected by corporations and government agencies. This role has existed in some form since humans began interacting with numbers, but a Data Engineer in a systems administration department has become an incredibly intricate, sophisticated role over the last 20 years. This profile is still very technical and excels at dealing with new products related to database administration.
3. Business Data Analyst
The third profile encompasses those who leverage data to improve businesses. At Ubiqum, this is the profile we specialize in through our Data Analytics & Machine Learning course. These skills will soon be expected and essential for any business leader, and they are a great springboard to move on to more advanced roles.
Throughout all of these profiles, Machine Learning plays a major role in processing large amounts of data and obtaining results that help improve all kinds of businesses.
How and when are Machine Learning algorithms used?
Machine Learning algorithms are an integral part of a more complex process — the business data analytics process, or CRISP-DM, the Cross Industry Standard Process for Data Mining.
This process consists of the following steps:
- Formulation of business target to be achieved through data analysis
- Creation, cleaning, preparation and preprocessing of a dataset
- Problem modeling using a Machine Learning algorithm
- Result analysis and iteration of steps 2, 3, and 4 until results are achieved (model training)
- Converting the results into valid and executable conclusions for the business
In this broader context, we can see that understanding how to use Machine Learning algorithms is necessary to become a professional data analyst, but it’s not everything.
What are the applications of Ubiqum’s Data Analytics & Machine Learning course?
Now that you know what Machine Learning algorithms are and what they are used for, the next question is: Where can I start using them?
At Ubiqum, we apply an innovative methodology to train our students in the intricacies of data analytics and machine learning. Our project-based learning takes our students through six different projects of increasing complexity that apply the main algorithms in use today, such as decision trees, simple, multiple, and logistic regressions, Random Forest and Nearest Neighbor (K-NN), K-Means, DBSCAN and Mean Shift, as well as time series analysis.
Ubiqum Code Academy: the academy of the future
At Ubiqum Code Academy, our main goal is to help our students start a new career in the digital economy.
A characteristically forward-thinking element of Ubiqum is our “learning by doing” methodology which focuses 100% on the practical, with no lectures or exams. You learn by programming from day one with the help of a mentor who is always on hand to guide you, and solve any issues that may arise throughout the learning process.
What kind of job do graduates of Ubiqum’s Data Analytics and Machine Learning course get?
Currently, Machine Learning is used for many different purposes, and in the future, the number of job opportunities will continue on their meteoric rise. New applications for data are continuously being discovered and the trend won’t slow down any time soon.
Among some of its applications, Machine Learning helps us to:
- Make a selection of potential customers according to the behavior they have had both on the website and on social networks
- Target files that are malware or spam anti-virus
- Identify the best time to publish content on social networks, when to make website updates or when to send newsletters
- Determine the ideal time to call a customer
- Make medical diagnosis predictions based on symptoms
- Know what updates to make to an app based on user behavior
- Identify fraud in transactions, such as PayPal’s anti-money laundering program
By filling in the form below, you can take the first steps in your new career as a data expert.