7 Reasons why it is Difficult to Learn Machine Learning

7 Reasons why it is Difficult to Learn Machine Learning

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In this blog, you will learn about the various challenges faced by programmers and developers to learn Machine Learning. Besides, you will also understand how you can overcome these challenges. But first, let’s understand the meaning of Machine Learning.

What do you mean by the term Machine Learning?

Machine Learning (ML) is one of the most demanding skillsets today. It is a subset of Artificial Intelligence (AI) that gives machines the ability to gain knowledge and improve past data and experience. This technology focuses on developing computer programs that have the ability to access the organization’s data and learn for themselves using the same with the help of Deep Learning.

Machine Learning begins with data from instructions or direct experiences where the systems search for data patterns to help make well-informed decisions in the future. The main goal is to allow systems to learn without any human guidance and make changes in their actions accordingly.

To learn ML, you can sign up for an online ML course to learn the significant concepts of this technology.

Let’s now read about individuals who are eligible to learn this trending technology.

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Who can learn ML?

Anyone interested in joining a Machine Learning course or wishes to build a career in this technology or both can learn ML. Moreover, professionals who must get a good knowledge of ML to grow and progress in their career include:

  • Data Scientists
  • Data Analysts
  • Business Analysts
  • E-commerce Professionals
  • Search Engine Professionals

Next, you will read about the various skills required to become proficient in Machine Learning.

Do you have the skills to become an ML Professional?

To become an ML professional, you need to acquire many skills. Below are some of the must-have skills for a career in this field:

  • Excellent programming skills in languages like Python, R, C++, or Java
  • Knowledge of statistics and probability
  • Understanding of applied mathematics and algorithms
  • Knowledge of distributed computing
  • Experience in using UNIX tools
  • Basic understanding of Hadoop and its applications

Let’s now read about the various job opportunities available for Machine Learning professionals this year.

10 In-demand ML Jobs in 2020

As per Gartner’s predictions, there will be over 2.3 million new job opportunities available by the end of this year in Machine Learning itself. So, it is safe to say that with the development of this technology, there is a pavement for creating many jobs.

Following are the 10 most demanding Machine Learning jobs available today:

  • Machine Learning Engineer
  • Artificial Intelligence Engineer
  • Data Science Engineer
  • Software Engineer
  • Data Scientist
  • Deep Learning and AI Researcher
  • Deep Learning Engineer
  • Research Scientist
  • Machine Learning Developer
  • Machine Learning Quality Analyst

Although these professionals are highly in-demand, there is not enough availability for them. This is one of the main reasons why they are one of the highest-paid IT professionals today.

Indeed, the growth rate of the average salary in this field was 344 percent from 2018 to 2019.

Further, find out why people consider it difficult to learn Machine Learning technology.

Why is it tough to learn Machine Learning?

Machine Learning is a complex technology which is why students, graduates, and professionals avoid learning it. You can witness ML and AI’s use in autonomous vehicles, self-tuned databases, and many such industries. Almost every field and domain have implemented ML and AI.

Due to these factors, developers and programmers are put under pressure to attain Machine Learning skills. Despite their interest and requirement to learn this technology, developers struggle to acquire the essential skills to master this technology.

Here, you will come across the seven most common challenges faced by professionals who wish to become proficient in Machine Learning.

Trouble While Applying Mathematical Techniques

Honestly, most individuals are either scared of math or have trouble using it in real-life applications. With reusable math functions and libraries, developers can now use mathematical techniques without much trouble. A regular programmer is not required to use complex mathematical techniques daily.

To learn ML, you must learn linear algebra, probability, and statistics since these concepts are the base of ML. If you are a programmer or a developer who wishes to become a Machine Learning professional, you need to start by brushing up on your mathematical skills.

Inability to Analyze Data

Other than math, you must have data analysis skills for ML. You should be able to process data and derive insights. Further, loading large sets of data, cleansing it to fill the missing data, dicing and slicing the dataset to discover hidden patterns are the most significant steps in the process of data analysis.

You need to learn about data visualization by working on histograms, pie charts, bar graphs, pivot tables, and more. Work and practice creating these visualizations in Microsoft Excel and understand numerous other visualization techniques that are available.

Confusion Between R, Python, and Julia

Programmers are generally confused regarding R, Julia, or Python to develop Machine Learning models. As a developer, you should choose the language you are most comfortable with. However, if you are a beginner who wishes to learn a programming language from basics, then Python is more preferable for Machine Learning.

Python contains a wide range of open-source tools and libraries that makes it easy to develop necessary ML models. Although previous statisticians and experts may recommend using R, Python is one of the easiest programming languages to learn and use. Julia is another language that has become popular, but Python is considered the best ecosystem for ML and other Data Science technologies.

Choosing the Correct Framework

Due to the availability of various ML libraries and frameworks, it might be challenging to choose the appropriate one to develop Machine Learning models. Python contains modules and libraries like NumPy, Seaborn, Matplotlib, Scikit-Learn, along with a few open-source toolkits, including Caffe2, Apache MXNet, and PyTorch.

In Python, you must begin with Scikit-Learn to develop basic models, after which you can use advanced toolkits like Caffe2. These tools are mainly used for advanced ML techniques like Deep Learning.

Availability of Multiple Approaches for the Same Issue

Once you learn to utilize the various tools and libraries, you might be confused regarding the algorithm you can use to solve a particular ML issue. This is where the best suited predefined algorithms are used to solve that problem. Besides, similar to other Computer Science branches, ML also provides different methods to solve the same issue.

You need to know the core concepts based on the algorithms and use your instincts to apply them in the given issues. Generally, several algorithms are used for evaluating the model’s accuracy before making a choice.

Lack in Advancement of Debugging and Development Tools

With the advancement in Integrated Development Environments (IDE), you can focus on the business issues rather than the environment configuration. Further, you can easily define a breakpoint in loops to check the variable state that alters every iteration. Tools like Eclipse and Visual Studio can help you speed up the software shipping process.

Unfortunately, most developer tools do not support ML. For this reason, you will have to use a completely different tool to develop ML models. Although there are robust tools like Jupyter Notebooks, they vary from traditional development tools.

It is difficult to debug ML models as compared to traditional programs. However, Microsoft and other IDE vendors work toward developing tools that offer a seamless developing experience. Although, experience in Jupyter Notebooks may help you build interactive applications in Python.

Availability of several Learning Resources

The massive open online courses (MOOC) and self-paced courses have hiked visibly in recent years. There are thousands of training programs available for you to learn ML. However, due to the variety of options, choosing the correct course may be a problem. Since ML is a vast topic, there are rarely any courses that consist of all the concepts. Yet, Intellipaat offers one of the best ML courses that contains all the significant modules of ML. Moreover, you will gain hands-on experience after working on industry-based projects. This will substantiate your learning experience and make you proficient in this technology.

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