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Edu4Sure Admin
- June 27, 2019
Whenever you hear this term Machine Learning, what comes to your mind first?
Well, engineers, computer scientists, robots and IT people often use this term. It is not for only IT people but anyone who wants to learn and become a pro!
What is Machine Learning?
It is the scientific research and study of statistical models, algorithms that computer systems use to perform a specific task without any unambiguous instructions. Also, machine learning is a subpart of “Artificial Intelligence”. The design is done to enable work on the given data and make decisions, predictions without any manual instruction given to the program.
Usage – It is used in Email filtering, computer vision.
Difference between Artificial Intelligence and Machine Learning
| ARTIFICIAL INTELLIGENCE | MACHINE LEARNING (ML) |
| AI – Artificial intelligence, where intelligence is called an achievement of knowledge and also have the ability to acquire knowledge. | ML refers to attaining knowledge or skill. |
| The technology aims to increase the chances of success and not accuracy. | The aim is to increase accuracy, but it doesn’t focus on success. |
| It works as a smart computer. | It is a simple concept wherein the machine takes data from the user and learns from it. |
| AI is designed to be a decision-maker. | In the case of ML, it helps to learn new concepts from the data. |
| In this system it is designed to mimic humans, to respond in certain circumstances. | It includes creating self-learning algorithms. |
| AI finds the optimal solution. | ML will find the solution regardless it is optimal or not. |
It is a system, hence it has many ways. let’s see the types of machine learning systems:
- Supervised machine learning algorithms
- Unsupervised machine learning algorithms
- Semi-supervised machine learning algorithms
- Reinforcement machine learning algorithms
Some facts: What exactly is Machine learning?
- The IIT Bangalore and Upgrade aim to create an 11-month program to prepare professionals in the field of data science and machine learning.
The few objectives of the PG programs are to learn classification algorithms, create intelligent solutions like Chatbots, smart games, etc. Besides, you can participate in hackathons and develop a portfolio for industry projects.
- Georgia Tech and edX offer an Online Master of Science in Analytics offering courses in Introduction to Analytics, Modeling, and Computing for Data Analytics, etc.
- The University of Illinois offers online courses in Masters in Computer Science and Data Analytics.
So you see this field is closely related to AI, Data Mining and Data Analytics and emerging widely. Hence, you must research first for the same.
Previously these subjects were not of much importance but now they have good salary jobs in the industry.
Importance of Machine Learning
This world of growing internet has led us to immense amounts of data. Where will so much data go?
To process such a huge amount of data, machine learning is important. It can quickly develop models that can analyze bigger, complex data and deliver accurate results – even on a very huge scale. Even Robotics might be used to create robots that store and retrieve data very efficiently.
Let the important tools required to create good machine systems:
- Data preparation capabilities
- Algorithms – basic and advanced
- Scalability
- Ensemble modeling
- Automation and iterative processes
For some of you these terms would sound quite remote, well those who keep a keen interest would understand the terms given above.
Use of Machine Learning
1. Financial Services
These companies use ML technology as a tool to get insights on data and prevent fraud. The insights give valuable information to the company related to decisions for trade and a lot more.
2. Government
The government uses this technology for public safety to detect fraud and avoid identity theft.
3. Health care
In the case of health care, it is used to process patient data. This, in turn, helps doctors to analyze data to identify trends that may lead to better diagnoses and treatment.
4. Retail
As we shop across various online websites, machine learning and data mining play a wide role in filtering the options for us. For example, if you’re viewing a product, a similar product shows up below under the recommended list. All these optimizations are the technology of machine learning.
5. Oil and gas
It is used to analyze the minerals in the ground. Additionally, the reorganization of oil distribution is done to make it more resourceful and cost-effective.
Now after discussing the fundamentals of Machine Learning, a question may arise in your mind. With the immense overflow of Internet Of Things today, “what can be the job profiles and salary for this stream?” Besides, do you know about IoT And Impacted Fields?
This technology comes under the stream of Computer Science. Therefore, to qualify for this subject the candidate should be thorough with basic computer science concepts, data structure, and algorithms.
If you take up Machine Learning (ML), you can be a/an
- Engineer in this field
- Senior ML Engineer
- Lead ML Engineer
- ML Engineer front office/back office
- ML Software Engineer
- Data Scientist
- Senior Data Scientist
- Senior Data Scientist IT
When you take up this course to study, it can bring many challenges in your path. Focus this course if you love to solve new problems and have a forte for technical stuff. Many times students take up this course and later realize this course was just not for them.
The average salary of an employee in this domain ranges from Rs. 60,000 to 1,00,000 monthly.
As this is a promising field, you may start from a small first and machine learning is not just computer coding! When talking about coding, Python cannot be left unmentioned. You can read more about Python The Language For Developers here.
FAQ: All About Machine Learning
1. Is machine learning hard to learn if I don’t have a coding background?
Not necessarily. It can seem overwhelming at first, but plenty of beginner-friendly courses start with simple concepts and use tools that don’t need deep coding knowledge. Over time, you’ll pick up Python and basic statistics naturally through projects.
2. How long does it take to actually understand machine learning?
Most people take around 6–12 months to get comfortable, depending on how consistently they practice. Real understanding comes when you start applying it, building small projects, predicting something, or analyzing data from scratch.
3. Can I make a career switch to machine learning after working in another field?
Absolutely. Many professionals move from finance, marketing, or operations into ML and data roles. The key is to learn the basics of Python, statistics, and data handling, then start with small real-world problems that connect to your old domain.
4. Do I need a master’s degree to work in machine learning?
No. While advanced degrees help in research-heavy roles, what really counts is your ability to solve problems using data. Employers look for portfolios, projects, models, and experiments, more than academic credentials.
5. How can I practice machine learning without a powerful computer?
You can use free cloud platforms like Google Colab or Kaggle, which let you run code online. They already have the tools you need, so you don’t need a high-end laptop to start experimenting.
6. What’s the future of machine learning, will it take away jobs or create them?
It’s doing both. Routine jobs are being automated, but ML is also creating new roles in data science, AI operations, model ethics, and more. People who learn how to use ML, even at a basic level, will stay in demand.
7. Where can I start learning machine learning the right way?
You can begin with free online material, but structured learning makes a big difference. Platforms like Edu4Sure offer practical training programs that focus on real projects, not just theory, helping you build confidence and career-ready skills from day one.
Learn more and move to large posts in the company to develop new technologies.
