Top Challenges Faced by Beginners in Machine Learning

Many learners want to master Machine Learning and don’t know where to start. It seems like a formidable task, especially if one lacks a thorough background. The idea would be simple: creating a learning journey always requires motivation and proper guidance. It would be easy to travel the path if we knew the hurdles that would cross our pathway! A better approach would be to learn from others' experiences that have already walked through that path.

This article will discuss some of the factors that can be obstacles in learning machine learning. Working around these obstacles can help us master and develop a long-term interest in this subject.

Lack of mathematical background

Getting started with ML need some basic understanding of mathematics, specifically linear algebra, calculus, probability, statistics, etc. The demand for mathematical knowledge expands as one dives into higher complexity. For example, in linear algebra topics like vector, vector algebra, etc., are some fundamental mathematical backgrounds needed to get started. Similarly, in calculus, a basic understanding of differentiation, partial differentiation, and chain-rule are essential first-level skills required to master machine learning. This involves understanding a loss function and how to use basic differentiation to optimize any function.

To sum it up, some math fundamentals needed for ML

• Vector algebra and operations
• Ordinary and partial differentiation
• Optimization techniques: functions with constraints
• Probability and Statistics for feature engineering and dimensionality reduction

Lack of coding background

Programming is a tool needed to implement ML algorithms, but one need not be a hardcore programmer to get started. But there may be hesitation to start machine learning if someone lacks past coding experience.

If you are entirely new to ML, it would be advisable to get some basic understanding of any programming language. Since Python has been used extensively for this domain and is user-friendly, we would suggest individuals lacking a coding background get some experience with Python. Python is easy to learn, and the internet is full of resources. 

Basic programming skills include learning to use loops, if-else statements, operands, functions, etc. Later in our blogs, we will show how to develop basic ML algorithms in python and use built-in libraries for complex problems. It is also essential to understand a code in the early phase of the coding journey. Once we get how algorithms are implemented, it would be easy to start the ML coding journey.

Lack of proper guidance and resource

While it is true that the internet is full of resources that offer a deep understanding of ML. But one might often feel lost in this sea of knowledge. One needs to break down the journey into parts to make things simpler. Another way of starting is to develop a basic understanding of ML terms and concepts before using them in algorithms.

• For example, knowing terms like gradient descent, regularisation, batch-training, and inference will help understand a simple neural network as one compiles all this stuff together. Understanding ML through familiar applications is one of the best ideas to learn machine learning.
• For example, we all know what an OR-gate is… So, if one starts with designing an OR-gate using Neural Network, they may feel more comfortable. Later they can extend this understanding to advanced applications.

The domain transition issue

At some stage of the professional journey, some people want to switch their interest to the machine learning domain but don't know where to start. The idea is simple: we need to start with concepts and then go to the algorithm. If you are not a beginner, you can also start with a real-world problem.

A suggested prerequisite would be developing an understanding of ML libraries like Scikit-Learn, TensorFlow, etc., using them in a sample dataset to observe the results and develop an understanding. At the same time, the long approach would be to start with an introductory ML course. One idea is simple: Think of Machine Learning as a tool to solve real-life problems and not just as a course.

The dilemma of what is important?

We have seen many good initial learners struggling with their ML models' accuracy and getting demotivated with the results. From an application point of view, model accuracy matters the most, but the algorithm is more important than the results from the learning point of view.

The accuracy factor depends on the model you have used, the process you used for data-preprocessing, and your system's most important configuration. Even if you have precisely followed the same path on two different machines, you may get varying accuracy.

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Tips to get started:

• Develop a basic understanding of math for ML
• Learn Python programming language, just enough to understand the basic implementation of algorithms.
• Start with ML concepts and later compile the concepts to understand ML algorithms.

Hope you have enjoyed the article!

Enjoy learning, Enjoy machine learning!