Is machine learning hard – An analysis

machine learning

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Generally, machine learning is known for its complex structures and algorithms. Innovative fields such as artificial intelligence, robotics, and machine learning are new. People often consider these branches too challenging to learn. Machine learning is one such field. There are many perceptions surrounding the subject.

The article uncovers all the beliefs and misconceptions around machine learning. To understand whether machine learning is hard or not, it is important to have in-depth knowledge of machine learning.

 

What is Machine learning? 

Artificial intelligence and machine learning are often used synonymously. Instead, machine learning is a branch of artificial intelligence that primarily focuses on replicating human intelligence. This means that machines can learn like humans. 

Arthur Samuel defined machine learning as “a computer’s ability to learn without being explicitly programmed.”

In simple terms, consider a system that collects data and analyzes the input data to give output values. Machines learn to optimize data according to new information. Machine learning systems improve performance over time through data analysis. Thus, its systems collect data, find patterns, and make better decisions based on analysis.

Some of the key areas where ML is used are self-driving cars, image recognition, speech recognition, medical diagnosis, and statistical arbitrage.

 

What makes machine learning hard?

Machine learning can be tricky and tedious, especially if you are a beginner. There is a reason most people consider machine learning hard and that reason is the vast variety of domains that fall under it.

Anyone who desires to make a career in machine learning needs to have basic knowledge of the following branches :

Deep Learning: Most concepts of Deep learning and Machine learning overlap. But both of them are different. Deep learning is about knowing the neural networks and the human brain, while machine learning is more about developing software.

Extensive Programming: Machine learning requires significant experience in Python, R, C++, or Javascript. Programming languages are the backbone of machine learning. Therefore, mastering these programming skills is necessary.

Maths Skills: Mathematical skills such as probability, linear algebra, and statics are used extensively in designing machine learning models. Additionally, understanding and analyzing bar charts, pie charts, and histograms can also prove to be helpful.

Algorithms: Software based on Machine learning only works when they have an optimized algorithm for every function. Mastering the art of designing efficient algorithms takes time, experience, and dedication.

Along with the above skills, one needs to be good at identifying and solving problems. 

 

How does machine learning work?

The process of imitating human intelligence into machines seems quite complicated. But that’s not the case. The entire machine-learning process can be divided into six steps :

  1. Data Collection and Preparation- Data collection is the most crucial step in the process. It decides the efficiency of the system. Machines learn by analyzing data. Thus, the more data, the better results. And the better the data, the better the results.
  2. Feature Selection and feature engineering- Feature selection includes cleaning data until it’s ready to ingest into the machine learning model.
  3. Selecting Machine Learning Algorithms- The computational and programming part comes here. The algorithm is embedded and tested in this step to get the desired results.
  4. Evaluating Models – Evaluating plays a significant part in fixing errors and improving programs. Although, evaluation is less tedious than the above ones. But it plays a prominent role in developing the program functionally.
  5. Parameter Tuning –Parameter tuning is the last process that measures the model’s efficiency. To be more specific, parameters are the predetermined metrics that help to find the maximum accuracy.
  6. Testing – Once all the programming is complete, the next step is to test and check the program. Developers test software/systems in various circumstances.

 

Most machine learning software/systems are developed using the above-mentioned steps. And because of such long processes, people sometimes find machine learning to be complex and hard.

 

How to get started with machine learning?

It takes four years to complete the bachelor’s degree in machine learning and the next two years to complete the master’s. Formal education is finished with a master’s degree. After that, you need to look for internships and fellowships to gain practical learning.

Formal education is one point to entering machine learning. There are multiple ways to kick-start a career in machine learning. If you already have a programming background, you can directly switch to machine learning. To do so you need to master all the required fields, such as deep learning and algorithm. Build a foundation in machine learning. Multiple free courses are available,  such as Google’s Machine learning crash course and Hinton’s Introduction to Deep learning. 

 

Conclusion

If you think machine learning is complex, there are certain things you should consider before coming to a conclusion. Machine learning demands mastering multiple aspects of mathematics, deep learning, and programming. Yes, sometimes it doesn’t seem very easy.

But don’t you think every new concept seems complex initially?

You will have to evaluate yourself. Know your core skills, strengths, and what new skills you need to learn to make a career in machine learning. Machine learning can be easy if you are willing to invest time and effort. 

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