How Can You Build a Career in Data Science & Machine Learning?
Machine Learning is the crux of Artificial Intelligence. With increasing developments in AI, IoT and other smart technologies, machine learning jobs are gaining higher exposure and demand in the technology market. If you are currently an IT professional, you might be interested in a career switch because of the exciting opportunities the industry offers to its aspirants. Or, you might have an interest that you have wanted to pursue long.
However, not knowing exactly how to start a career in machine learning can lead an aspirant in the wrong way. There should be a proper agenda on how to identify the right opportunity and approach it in a systematic way. In this article, let us see some of the essential steps that one can take towards their machine learning journey.
Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. Artificial Intelligence is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.” Machine learning is a subfield of artificial intelligence, which enables machines to learn from past data or experiences without being explicitly programmed.
Machine Learning enables a computer system to make predictions or take some decisions using historical data without being explicitly programmed. Machine learning uses a massive amount of structured and semi-structured data so that a machine learning model can generate accurate results or give predictions based on that data.
is a subset of machine learning where algorithms are created and function similar to those in machine learning, but there are numerous layers of these algorithms, each providing a different interpretation to the data it feeds on. Such a network of algorithms is called artificial neural networks(ANN), being named so as their functioning is an inspiration, or you may say; an attempt at imitating the function of the human neural networks present in the brain.
It is time to get accustomed to the general process in a data science project. It always starts with finding a valid business use-case depending upon the industry you are working in. Next, we need to find the data to support the business problem. This usually requires the Data Engineer to develop ETL scripts in tools like Informatica or Talend that will connect to the data source(s) and retrieve data. Usually, data sourcing can be a challenging task if there are security constraints in place as per company policy.
Data can be either in textual format stored in flat files or RDBMS databases. Or, it can be in the form of video or audio files. At this stage, the data analyst explores the data in columns and rows look for obvious issues like duplicates and missing data. The data manipulation or cleansing can make up to 70% of the project’s time depending on the amount of pre-processing that needs to be done.
Next, the machine learning engineer/data scientist applies algorithms like regression, classification, segmentation, etc on the data and measures the accuracy metrics. it is expected that different models can be generated that give different performance measures. The final model is selected and deployed in the production environment.
To get into a field in Data Science/Machine Learning, you must have a strong technical background in one or more of the following topics. Remember, this is a highly technical field and one must be prepared to have the requisite educational qualification before starting the
Mathematics: Behind numerous standard models and constructions in Data Science there is mathematics that makes things work. It is important to understand it to be successful in Data Science. It includes crucial fields such as Discrete Mathematics, Calculus, Linear Algebra, and Probability.
