We often describe intelligence as the ability to work efficiently or to solve problems. However, this concept of adopting intelligence is now changing in the IT world – it is leading to the development of artificial intelligence (AI) and ushering in the fourth industrial revolution.
The impact of AI in society is transformative galvanizing in the areas of finance, transportation, medical research, space exploration and meteorology hanging – it is driving the development of artificial intelligence (AI) and bringing about a fourth industrial revolution.
ML and AI
Artificial Intelligence, in short, AI, is a field of study in automation industries. Conceptually, AI adopts technological means to develop intelligent machines. And machine learning, ML, is one of the ways of executing the concept of AI.
Machine Learning is a branch of Artificial Intelligence and is a vast field of study. It inherits the principle from artificial intelligence aimed at training machines. ML deals with developing computer algorithms that let the computer programs automatically improve machine intelligence through experience.
ML field focuses on synthesising meaningful concepts, making them practically implementable from historical data. It involves a mechanism of automatic and periodic learning by acquiring skills, knowledge and deriving the right decisions from a series of experiences. However, its learning scope could be the overall field of study or specific techniques that address the objective.
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With strong roots in statistics, Machine Learning is becoming one of the most interesting and fast-paced computer science fields to work in. As a subject of study, Machine Learning mainly focuses on different algorithms, their working based on mathematics, and implementing the algorithms in a programming language.
Unlike traditional programming, ML development need not be explicitly programmed. The algorithms train the programs (machines) to behave smartly. Machine learning thus allows us to determine patterns and develop models for tasks that are hard for humans to handle.
Machine learning is being applied to monotonous as well as complex logic-based processes. The implementation of ML in the industry enhances performance in more efficient and intelligent ways. The application of ML in the industries is limitless.
For example, some of the everyday life tasks performed via the web, say, chatbots, image recognition, ad serving, search engines, fraud detection, spam filtering, etc., work on machine learning models.
Industry Adoption of AI
Digital evolution has boosted the adoption of AI in the technology industry. Besides big players like Amazon and Google, even smaller startups are focusing on AI-focused development in their business. The adoption of ML algorithms primarily to improve the customer experience has resulted in a magic transition in the market.
Evolution of AI
In 1935, British computer pioneer, Alan Turing, described a machine with unlimited memory and scanners that went through these memories, symbol by symbol, reading and writing more symbols, which would be indicated by the instructions stored in memory as the scanner symbol. This is the Turing machine, which is the foundation of modern computer systems.
Since then AI has developed rapidly. In 1945, Turing predicted computers would play excellent chess.
By 1977, Deep Blue, a chess program, beat the world champion, Garry Kasparov.
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Uses of ML
ML has omnipresence in the industry. It is widely used in various sectors, including IT-based production, research, medical, marketing, and so on.
ML is now used in major financial analysis and decisions, including share price prediction, electronic trading, loan risk assessments, real estate valuation, etc.
AI is also greatly used in telecom, satellite, and GPS. It is vital in space explorations, including the ongoing NASA Mars Perseverance Probe.
In the medicine domain, it is used to detect heart and lung ailments, and also treat cancers.
In agriculture, it is being used to predict the most efficient harvest season. It also has a presence in automobile manufacturing, and in market research businesses to tackle targeted marketing and adoption of online searches in several other sectors.
Machine visual perception is used in surveillance and tracking. Some courts in the US now use the algorithms of ML models to decide the chances of defenders becoming repeat offenders.
The ML technology is also used to make deepfakes, now experiential on humour ground, however, over time, it may cause a threat, especially like fake news.
Market Demand for AI
According to the Gartner Report of 2021, by 2025, 50% of large enterprise IT leaders will need Operations Technology Management (OTM) skills to support artificial intelligence (AI) and enhanced intelligence.
As per IDC, the figures in forecast growth for the global AI market will be up by 16.4% year over year in 2021 to $327.5 billion. Also, by 2024, the market is expected to break the $500 billion mark with a five-year compound annual growth rate (CAGR) of 17.5% and total revenues reaching an impressive $554.3 billion.
In the Indian context, the IDC report cited the growth of artificial intelligence spending by over 30%. The AI spending is likely to grow from $300.7 million in 2019 to $880.5 million in 2023 at a CAGR of 30.8 %.
Salary in AI
As per PayScale, the average salary for professionals in Artificial Intelligence (AI) is Rs1,546,314 and for ML engineers, ₹800k. The average machine learning salary in India is approximately Rs. 6,86,281 per year, inclusive of incentives.
It has been found that an AI Engineer gets a lucrative hike of up to 60–80% when switching jobs, whereas the other stream professional could bag an average of 20–30%.
In-demand Machine Learning Skills
Professionals in AI can have one of the roles in the following title:
- Big Data Engineer
- Business Intelligence Developer
- Data Scientist
- Machine Learning Engineer
- Research Scientist
- AI Data Analyst
- AI Engineer
- Robotics Scientist, etc.
Who can Become an ML Engineer?
A math-savvy student with a flair in coding is the most desirable candidate to choose a profession in the AI arena. Graduates with mathematics and/or statistics background may opt for becoming ML engineers. A minimum Bachelor’s or Master’s degree preferably in mathematics or statistics, if not computer science, data science, software engineering is required. Having hands-on expertise in mathematics-based programming languages, such as Python, R, or equivalent is a plus point in ML.
- The knowledge of statistics and probability principles set the foundation of many ML algorithms.
- Besides numerical concepts, having fundamental concepts of software engineering clear would ease the implementation.
- Inclination towards working with different ML algorithms and libraries is essential.
- Get the knowledge of data modelling and evaluation methods that would help practice sample ML projects.
- There are a lot of online avenues to participate in online coding forums and learn more about ML fundamentals.
In addition to having ML skills and the capacity of managing AI-based projects, industries look for certifications in ML/AI courses. Therefore, get enrolled in an official course that fits you. The majority of online artificial intelligence and machine learning courses are available to opt for.
One of the reputed institutions named upGrad would be to your rescue. You can benefit from the courses upGrad offers. Choose one of the online courses in AI and ML and be a professional ML Engineer after joining online and see achieve your dream.
Over the decades of successful transition into e-learning, several online channels ease students to enrol in the desired course. There are several providers that offer AI and ML courses to help professionals acquire credentials in their field of study. A brand named upGrad is one such pioneer provider of technical and business-related online courses, including AI and ML courses.
Courses offered by upGrad
Given that the technological revolution is led by machine learning and artificial intelligence, upGrad has come up with cutting-edge, case-based artificial intelligence and machine learning courses for data science aspirants and professionals. There are four major courses in Machine Learning available at upGrad.
- Advanced Certificate in Machine Learning and Deep Learning – Become an ML Engineer by learning how to build a chatbot, a news recommendation engine, and lots more
- Advanced Certificate in Machine Learning and NLP
- Executive PG Program in Machine Learning and AI –Take the Machine Learning Engineer courses and learn how to train an agent to play tic tac toe, train a Chatbot, and lots more.
- Master of Science in Machine Learning and AI – Pursue an integrated Master’s Program in artificial intelligence and machine learning courses from IIIT-B and LJMU. It is 10 times more economical than offline programs.
- Advanced Certificate Programme in Machine Learning – Pursue coveted opportunities in machine learning and artificial intelligence from IIT Delhi and strengthen your knowledge of basic data science concepts. It teaches you the underlying mathematics of ML implementation, handling imbalanced data and familiarises you with evaluation metrics and optimisation strategies of ML algorithms. For more details, visit our website.
All courses are online and are designed for working professionals.
The Eligibility criteria benchmarked as a min Bachelor’s Degree with 50% or equivalent passing marks. Students having a minimum of 1 year of work experience or a degree in Mathematics or Statistics are more suitable.
Why choose upGrad Courses?
The AI and ML courses are approved by WES (World Education Services) and accredited with IIT Bangalore, a deemed university by UGC, AICTE approved. As per NIRF Rankings, the institute stands in the top 70 Engineering Universities.
The curriculum is designed by the best-in-class experts and leading faculty members. The content includes multimedia, videos, case studies, and projects.
Differences Between AI and Machine Learning
Before delving into AI and ML courses, let’s establish a crucial fact: AI and ML are distinct entities with significant differences.
Simply put, AI encompasses a wide range of applications that aim to mimic human-like intelligence, while ML amplifies the decision-making capabilities of such applications. In essence, AI serves as a broader concept.
However, it is common to encounter these terms used together in AI and ML courses, often collaborating in various applications. An illustrative example of this collaboration is evident in search engines. When you input a query in the search bar, machine learning algorithms predict what you might be searching for.
For a clear understanding of the differences between AI and machine learning, refer to the table below:
|Aspect||Artificial Intelligence (AI)||Machine Learning (ML)|
|Scope||AI encompasses various techniques, including machine learning, natural language processing, computer vision, expert systems, and more.||ML deals explicitly with algorithms that learn patterns from data and make predictions or decisions based on that learning.|
|Data Dependency||AI systems may or may not rely on data. They can be rule-based or heuristic-driven.||ML heavily relies on data for training and improving the model’s performance.|
|Human Intervention||Some AI systems may require continuous human intervention for decision-making.||ML systems aim to reduce human intervention by learning patterns and making decisions independently.|
|Main Objective||To create machines that can perform intelligent tasks without human intervention.||To enable machines to learn and improve performance on specific tasks using data.|
|Complexity||AI can encompass both simple rule-based systems and complex self-learning models.||ML can range from simple linear regression to sophisticated deep learning architectures.|
|Decision Making||Some AI systems may use predetermined rules and decision trees.||ML systems make decisions based on patterns and correlations learned from data.|
|Adaptability||AI systems may or may not adapt to new data or environmental changes.||ML systems are designed to adapt and improve with new data and feedback.|
|Implementation||AI implementation can be rule-based, heuristic-based, or data-driven (ML-based).||ML implementation revolves around creating algorithms that learn from data.|
|Purpose||AI aims to mimic human intelligence and solve complex problems.||ML is designed to handle specific tasks efficiently by learning patterns from data.|
How to Get the Most Out of Your Online Course?
If you opt for online artificial intelligence and machine learning courses, these tips are invaluable to ensure your success.
- Cultivate Self-Motivation: Online artificial intelligence and machine learning courses demands considerable self-discipline to complete the course. Without the traditional class structure, taking responsibility for your progress is crucial. Consider sharing your achievements on social media or discussing your course progress with friends to keep yourself accountable and motivated.
- Engage in Discussions: Interact with your fellow learners within the machine learning engineer course. Share your insights and inquire about their experiences, including any mistakes they encountered and suggestions for overcoming challenges. Participating in discussions can help you avoid common pitfalls and accelerate your mastery of the material.
- Seek Clarification: Don’t hesitate to ask questions when you encounter doubts or uncertainties. Many online courses offer doubt-clearing sessions; some provide instructors’ contact information for inquiries. Be proactive in seeking help, whether solving an assignment or grasping a specific concept.
- Master Time Management: Set short-term goals to stay on track with your learning journey. Establish weekly objectives and allocate a specific amount of coursework to complete daily. This approach allows you to monitor your progress effectively and ensures timely course completion.
Enhance your industry-ready skills and knowledge by enrolling in one of the top online courses in machine learning engineer course offered by upGrad.
Popular AI and ML Blogs & Free Courses
Now that you have a fair idea of the importance of AI and ML courses, you can decide on studying machine learning. Get information on where to learn machine learning, how to start learning machine learning, as well as the best way to learn machine learning.
Learn ML Courses from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career.
The course provider institution, upGrad provides a Executive PG Program in Machine Learning and AI and a Master of Science in Machine Learning & AI that may guide you toward building a career. These courses will explain the need for Machine Learning engineer course and further steps to gather knowledge in this domain covering varied concepts ranging from Gradient Descent to Machine Learning.