Machine Learning is a subset or an application of Artificial Intelligence that allows systems to automatically learn, improve and predict outcomes without being clearly instructed or programmed. Machine Learning aims to allow computers to learn and work on their own without human intervention or help.
This kind of learning is done by observing data which includes analyzing examples, instructions or experiences. This enables understanding the patterns in data and making better decisions based on examples.
With its growing possibilities and popularity, more and more people and organizations are becoming interested in Machine Learning these days.
It is quite interesting to see that more than 75% of businesses have invested in Big Data, and this investment is going to increase the role of Machine Learning dramatically over the next five years. You will also be quite overwhelmed to know that the worldwide revenues for Artificial Intelligence (including Machine Learning) will increase to more than US $46 billion in 2020.
These figures are quite fascinating and intriguing enough to make one choose a career in Machine Learning. But before diving into the world of Machine Learning, it is important to understand the key skills that Machine Learning demands from individuals. The primary thing that one must understand is that Machine Learning ideally requires experience in both, software engineering as well as data science.
Essential Skills Required
- Fundamentals of Computer Science and Programming Skills
Computer Science Fundamentals include topics such as data structures, algorithms, computability and complexity, and computer architecture.
Programming skills include knowledge of languages such as Python, R, JAVA and C++.
- Thorough knowledge of Probability and Statistics
Probability forms the basis of Machine Learning algorithms and allow you to deal with uncertainty. The major topics include Conditional Probability, Bayes theorem, Likelihood and Markov Decision Processes.
Statistics is necessary to build and validate models from the obtained or observed data. Major topics include measures, distributions and analysis methods.
- Understanding Machine Learning Algorithms and its Libraries
Though Machine Learning algorithms are easily available through libraries or APIs, it is also important to apply them appropriately and effectively. This involves the understanding of a suitable model and the corresponding learning procedures. This also involves awareness about the pros and cons of different methods and approaches.
- Expertise in Data Modeling and Evaluation
Data modeling basically refers to the analysis of data to find the hidden patterns and predicting the properties of unknown cases. This estimation process involves the evaluation of the Machine Learning models as well.
- Software Engineering Skills
The ultimate deliverable for a Machine Learning professional is software. This includes the application of Software Engineering best practices which eventually increase productivity and quality of products. Also, a system design built carefully serves well and lets you avoid unnecessary bottlenecks.
Need for Training and Certifications
These skills can be best acquired and evolved with the help of training courses and certifications in Machine Learning. There are various options to choose from and it is important to opt for the most suitable and appropriate course and MCSA certification. The most popular certifications have been listed below.
- Introduction to Machine Learning (from Cloudera)
- MCSA: Machine Learning (from Microsoft)
- MCSE: Data Management and Analytics (from Microsoft)
- Perform Cloud Data Science with Azure Machine Learning (from Microsoft)
There are various other customized courses from many training institutes and universities. A complete and thorough research is required before selecting and enrolling for any course.