In today’s disruptive atmosphere, an analytical approach towards decision-making has helped businesses all around the world pursue growth and scalability. As numerous job opportunities are being created because of this substantial shift, Machine Learning is fast becoming one of the most indispensable tools to achieve business goals. The market size for Machine Learning has grown exponentially and is further expected to reach USD 8.81 Billion by 2022 at a Compound Annual Growth Rate (CAGR) of 44.1%.
ML is a section of Artificial Intelligence that eliminates programming for every step. As ML automates data analysis based on algorithms that do not require repeated programming, it is preferred in many organizations to ease the work of data scientists, statisticians, AI engineers, and so on.
The raw data that is received is easily transformed into useful information by the machine to accurately forecast potential results. A large number of applications that involve data mining are developed using Machine Learning and its algorithms.
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What is Machine Learning?
Machine learning is a division of artificial intelligence (AI) that deals with supervised or unsupervised learning from available data and building functional accuracy over time.
Machine learning involves training algorithms to identify trends or patterns in large-scale data to provide insights and improve precision in prediction. It is an incredibly vast field that allows almost every industry to avail the development of computer systems to drive growth and scalability. Machine Learning uses algorithms and models that are based on statistics. These form the foundation of extracting accurate information from available data.
Learning algorithms are of three types:
1. Supervised Machine Learning Algorithms: These are used to conduct predictive analysis by identifying patterns in data points value labels.
2. Unsupervised Machine Learning Algorithms: Here, we have data in the form of clusters. This organized data allows the analysis of complex data structures.
3. Reinforcement Machine Learning Algorithms: In this algorithm, we choose actions based on data points.
An engineer with expertise in machine learning is capable of programming applications that can retrieve useful information from arbitrary data.
How Long Does it Take to Learn Machine Learning?
Having gained an insight into the lucrative prospects Machine Learning as a discipline brings, we will now determine how long it takes to master Machine Learning.
Machine learning courses vary in a period from 6 months to 18 months. However, the curriculum varies with the type of degree or certification you opt for.
You stand to gain sufficient knowledge on machine learning through 6-month courses which could give you access to entry-level positions at top firms. You gain clarity with respect to the fundamentals of Machine Learning, Python, SQL and are introduced to other concepts like NLP, Deep Learning, Data Analysis, etc.
However, more is always less when you are pursuing a dynamic field such as Machine Learning that has positively impacted Education, Finance, Computer Science, Supply Chain and a diverse range of other industries. And to advance further in your career and pursue higher profiles in the field, you will require substantial expertise and hands-on experience.
Careers in Machine Learning
The advent of digitalization has been revolutionary in almost every field. This radical change has brought a new wave of software engineers who are inclined to pursue ML and AI to strive ahead in their careers. Luckily, there is a rewarding career path for software professionals with expertise in machine learning:
Here are the popular career options you can consider after completing a Machine Learning course:
1. Big Data Engineer
Average Base Salary: ₹808K /year
A Big Data Engineer is responsible for maintaining the Big Data of an organization. In this epoch where every work is dependent on data, data gets stored on the cloud. It has to be administered by someone so it can be retrieved as needed. This work is done by a Big Data Engineer and Machine Learning plays a significant role as the algorithms used in retrieving data are learned through ML.
2. Business Intelligence Developer
Average Base Salary: ₹514K /year
Along with business acumen, you can learn Machine Learning to enhance your excellence in handling a business. This confluence is imperative to managing and regulating data in a manner that the profits of an organization increase.
3. AI Data Analyst
Average Base Salary: ₹1320K /year
Data Mining, Data Interpretation, and Data Cleaning are a few skills required to be an AI Data Analyst. Other than having good knowledge of Mathematics and Computers, ML is another prerequisite to be able to interpret data correctly.
4. Product Manager
Average Base Salary: ₹1407K /year
There is a requirement of product managers in every field which makes this profile highly sought-after. It can be a product-oriented industry or a service-oriented one. As a product manager, you will need to collect and interpret data. To interpret data, expertise in Machine Learning is required to enhance the functionality of an organisation.
5. Research Scientist
Average Base Salary: ₹702K /year
If you are inquisitive and tend towards research, pursuing Machine Learning will empower and boost your skillset. A background in Machine Learning can earn you an influential position in the Research and Development Department of an organization.
6. Machine Learning Engineer
Average Base Salary: ₹686K /year
If you can manage big data and work with predictive models, this is the right profile for you. Machine Learning Engineers are constantly in demand in fields that require prediction-based models of imaging, speech recognition, and cybersecurity, among others.
7. Data Scientist
Average Base Salary: ₹817K /year
Expertise in Machine Learning along with competence in multiple programming languages can enhance your skills in predicting patterns in large amounts of data. It is a data scientists job to predict consumer buying patterns, purchasing habits, etc to help organisations make data-driven, intelligent decisions to drive conversions and sales.
8. AI Engineer
Average Base Salary: ₹839K /year
AI Engineers are problem solvers who use Machine Learning algorithms along with the neural network to devise AI models that can predict the data correctly.
Machine Learning is a rather logical subject and is part of the curriculum of Computer Science degrees. Even though academic projects can impart in-depth knowledge of its practicality, when it comes to machine learning, it requires perseverance and extensive learning to master the subject.
This is possible through online courses, or a specialized course augmented with other disciplines such as Artificial intelligence and Software Development Life Cycle, among others.
The qualifications and skills include:
- Basics computer skills
- Programming languages such as C, C++, Python, and Java
- Data Modeling
- Data Evaluation
- Fundamentals of software engineering
- Perception of Data and Statistics
- Mathematical skills
Apart from these technical skills, here are a few intangible skills that are paramount to achieve success in this field.
- Problem-solving skills
- Analytical capacities
- Logical reasoning abilities
Future Scope of Machine Learning
With companies adopting Artificial Intelligence and Machine Learning at the base of their operations to increase efficiency and pace, the future promises an even greater increment in job opportunities in this field. As per Gartner, 2.3 million jobs have already been created worldwide and by 2025, there will be a whopping 2 million new job opportunities for aspirants to pursue.
This brings us to the end of the article. For more information on Machine Learning concepts, get in touch with the top faculty of IIIT Bangalore and Liverpool John Moores University through upGrad‘s Master of Science in Machine Learning & AI program.
What skills are required for machine learning?
In order to do machine learning well, you need to have a good understanding of statistics, stochastic modelling, linear algebra and calculus. These are obviously not required in depth but only a working knowledge is needed. What's even more important than the technical knowledge is a solid understanding of algorithm theory. Also, it's quite important to develop a good intuition for probability theory, which will help you to understand how to formulate the problem, instead of coming up with a solution.
Which programming language is used for machine learning?
Machine Learning is a subfield of Artificial Intelligence (AI) which gives computers the ability to learn without being explicitly programmed. There are many programming languages which can be used to develop Machine learning models. The most popular are: C, C++, Python, R, MatLab, Octave, Perl, Java, Scala, Julia etc. The techniques and applications of machine learning are increasing. It is used in many parts of machine learning, such as data mining and statistical modelling. The main purpose of these languages is to improve the efficiency of machine learning and make it easy.
What are the applications of machine learning?
The applications of machine learning are almost unimaginable. I listed a few of them below: Product recommendations (Amazon and Netflix) Fraud detection (e.g. PayPal) Credit card risk assessment (e.g. JPMorgan) Marketing Campaigns (e.g. Facebook Ads) Medicine: Cancer treatment, heart disease treatment etc. Machine learning is already transforming the way companies work, and it will continue to expand into more mainstream markets. Because of its potential, lots of US companies are already using it to gain an advantage over their competitors.