Introduction to Machine Learning
Machine learning is the root of artificial intelligence. With the increasing development in AI, IoT and other smart technologies, machine learning jobs are gaining high exposure and demand in technology market. If you are currently an IT professional, you might be interested in Career Switch as the industry offers exciting opportunities to its candidates. Or, maybe you have an interest that you've wanted to pursue for a long time.
However, not knowing how to start a career in machine learning can lead a candidate to a wrong path. There should be a proper agenda on how to identify the right opportunity and how to approach it systematically. In this article, let us take a look at some of the essential steps one can take towards their machine learning journey.
AI, Machine Learning and Deep Learning
Artificial intelligence is science and engineering of creating intelligent machines, especially intelligents computer programs. Artificial intelligence deals with the similar task from using computers to understand human intelligence, but AI does not limit 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 explicitly programmed.
Machine learning enables a computer system to make predictions or make certain decisions using historical data without being explicitly programmed. Machine learning extensively uses structured and semi-structured data so that machine learning model can generate accurate results or make predictions based on that data.
Deep learning is a subset of machine learning where algorithms are built and perform similar tasks to machine learning, but these algorithms have multiple layers, each providing a different interpretation of the data. Such networks of algorithms are called artificial neural networks (ANNs), so named because their methodology is an inspiration, or you can say; Attempts to mimic the function of the human neural network present in the brain.
Data Science Process
It's time to get accustomed to the normal process in a data science project. It always starts with finding a valid business use-case, depending on the industry you are working in. Next, we need to find the data to support the business problem. This usually requires a data engineer to develop ETL scripts in tools such as Informatica or Talend that will connect to the data source and retrieve the data. Generally, data sourcing can be a challenging task if security is lacking as per company policy.
The data can be either in text format which is stored in flat files or RDBMS database. 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 and looks for obvious issues such as- duplicate and missing data. Data manipulation or cleaning can make up up to 70% of the project time depending on the amount of pre-processing that needs to be done.
Thereafter, the Machine Learning Engineer and Data Scientist apply algorithms like regression, classification, segmentation etc. on the data and measure the accuracy metrics. It is expected that different models can be produced which give different performance measures. The final model is selected and deployed to the production environment.
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