Machine learning provides a scalable environment for implementing, deploying and utilising technologies unheard of until the last decade. The recent developments in technological aspects and efficient software and tools have made machine learning mainstream. The era of machine learning, data science, artificial intelligence and other big data analytics brings forth a new set of algorithms, principles, opportunities and challenges. Humans are witnessing a similar revolution to that of the Internet in terms of data science methodologies. Much credit for high-ended and futuristic technical solutions can be given to the machine learning trends of 2020 that have changed lifestyles for the better.
Machine Learning: Why all the Hype and What are its Advantages?
“What Artificial Intelligence and Machine Learning allows you to do is find the needle in the haystack.”
The quote mentioned above by Bob Work highlights how machine learning is solution-oriented and focused on providing fixes to issues. Given the potential and vast horizon that machine learning covers, it’s not surprising that this data-backed domain will influence the market in the coming years. Professionals looking to move into this sector can enrol for a machine learning and big data analytics certification course.
As per the 2020 report generated by McKinsey, 49% of companies worldwide explore and plan to use Machine Learning in their work-environments.
The stat above zeroes in on the speed at which machine learning is taking over the job market. Therefore, having robust understandings about its concepts, elements, practical tools, and algorithms is a must for budding machine learning engineers. A well-planned and informative program such as the machine learning course from IIM Raipur is an excellent source for learning about new advancements in this sector and its advantages in transforming lives around us.
Here are some advantages that machine learning provides:-
- Ability to Identify Trends and Patterns
Machine learning can survey large amounts of data and can find similar trends and patterns. Identification of trends and patterns works well for retail and e-commerce companies that need to keep a tab of customer’s preferences and interest to deliver consumer-focused products.
- Performs Tasks on It’s Own (Automation)
Another advantage of machine learning pedagogy is that it’s able to perform tasks independently. Machine learning systems and networks are capable of predicting and generating results. Automaton works well for large corporations and businesses who have complex work-flows, and with machine learning, these can be automated.
- Wide Range of Applications
Applications of machine learning are humongous and varied. Be it providing customised recommendations, self-driven cars, speech conversion software or intelligent instructions based platforms; machine learning covers everything.
- Are Continuously Evolving and Learning
Machine learning possesses the ability to learn by itself, and upon generating or predicting results, the system gets better and improves its outcomes. With data surveyed each time, the machine learning model becomes robust.
These advantages help in performing complex tasks for companies and render salient applications for human’s private lives. Understanding core particulars through the machine learning course from IIM Raipur will allow executives to engineer models and applications that help make lives easier and comfortable. However, before enrolling for these courses, it’s essential to understand the life-cycle of machine learning.
What all Comprises the Machine Learning Life-Cycle?
As mentioned earlier, machine learning offers the luxury of carrying out tasks automatically and generates meaningful insights. However, all this can only be done via a brilliantly planned machine learning process. Certain steps comprise the machine learning cycle. Professionals enrolled in machine learning and big data analytics certification course can learn about the following steps involved in the machine learning life-cycle:-
- Step 1 = Accumulating Data
The basis of machine learning is based on collecting data from various sources. Collecting varied data can be encapsulated in a specific data set. Engineers must ensure that quantity and quality of data collected is reliable and resourceful since data determines the outcome.
- Step 2 = Preparing the Data
Allotting and segregating data accordingly for machine learning training is the next step after collecting the data-sets. Preparation of data includes its exploration and pre-processing. Data exploration involves an understanding of the nature of data/information that’s being analysed.
Next up, upon realising data’s nature, it’s pre-processed for further analysis.
- Step 3 = Performing Data Analysis
Amongst the most critical steps of machine learning is conducting data analysis. Clean and segregated data is now analysed based on analytical techniques that need to be used. In addition to a selection of technology, models are devised that help in solving the client’s purpose.
Several machine learning techniques such as- classification, regression analysis, clustering and association come into play for developing a machine learning model.
- Step 4 = Training and Testing Model/s
Post-development and deployment of machine learning models, they are first trained and then tested. Data-sets are used for training models using different algorithms. Just like training makes an employee better at his/her role, the same goes for training machine learning models.
Post-training, testing of models is done. Testing involves checking a model’s accuracy against the results generated. Professionals will be hands-on experience of training and testing models through the machine learning course from IIM Raipur.
- Step 5 = Deploying the Machine Learning Model
The last step of the machine learning life-cycle is the model’s deployment. Model synthesised in the above steps is introduced in the market. However, post-deployment, machine learning engineers must ensure that the model developed is accurate and performing at the required speed.
Deployment of machine learning model is similar to the generation of the final project.
Machine Learning Life-Cycle is Complete!
Machine learning life-cycle involves the steps as mentioned above. Starting from collecting required data, moving on to its preparation, conducting data analysis, training models against several data-sets and giving it a dry-run and finally deploying the machine learning model comprises the life-cycle.
Executives keen on building their skill-sets and knowledge about this sector can enrol for a reliable machine learning and big data analytics certification course. Moreover, those professionals who want to move into prosperous machine learning or big data analytics career roles must sign up for required courses. The machine learning course from IIM Raipur provides key insights about these roles to professionals.
