Machine learning has grown from a niche area of computer science into one of the hottest skills in demand across industries. You can find algorithms that use machine learning from everything from predicting how customers will behave, to powering recommendation engines that influence what you purchase. Machine learning (ML) naturally became part of AI applications, and when demand for ML skills skyrocketed, thousands enrolled in a Machine Learning Course.
As understandable as this excitement is, most students encounter typical but avoidable mistakes novice students make when getting started. If you are new to learning machine learning, understanding these mistakes can help you exponentially improve your learning curve and to gain a firm foundation in machine learning.
In this article, we will discuss five major mistakes beginner students make in machine learning, and more importantly discuss the solutions to these problems.
1. Skipping the Fundamentals of Mathematics
One of the biggest pitfalls for newcomers to the realm of machine learning is disregarding the essential mathematics related to ML algorithms. There are a lot of linear algebra, statistics, calculus, and probability in machine learning. If you don’t have a reasonable command of these topics, you may struggle to render meaning to basic ML concepts.
Why It Happens:
Many beginners seem eager to get coding and building machine learning models, assuming they can learn the math later. While it is true that you can learn ML without being a math specialist, you will not be able to fully understand how algorithms work without math knowledge, which can hinder your ability to fix bugs, and diagnose performance problems with your models.
How to Avoid It:
If you are beginning with little to no math knowledge that pertains to machine learning, I encourage you to pause for a moment and build a strong foundation. The first thing you should do, before beginning a Machine Learning Course, is spend some time learning about the basics of:
- Linear Algebra: Vectors, matrices, and their operations
- Calculus: Derivatives, gradients, and optimization techniques
- Probability & Statistics: Bayes’ theorem, distributions, hypothesis testing
There are many online resources and free courses available to help you understand these fundamentals. If you do decide to take a Machine Learning Course, there is still a good chance that you will be introduced to the math needed, so make sure that look over the syllabus before you start.
2. Focusing Too Much on Theory and Not Enough on Practice
Machine learning theory is useful and important, but it’s easy to theorize without ever applying it to real-world problems. Beginners often spend so much time working on studying theoretical algorithms, that they don’t actually spend time coding or working with datasets, which makes gaining real experience very difficult, and without real experience, it is not possible to get a good sense of machine learning.
Why It Happens:
It is normal for you to want to have a solid theoretical understanding of ML before you move on to the application of it to practical situations in the real world, however, learning ML is an iterative process, and you certainly cannot learn it by just reading textbooks or watching videos. Theory is important for you to learn about the subject area, but without any hands-on experience, you will forget what it is or get overwhelmed with the complexity of the subject.
How to Avoid It:
You just want to ensure that your Machine Learning Course has a good balance of theory and the application of learning it to real-life scenarios. Practical experiences are important to implement what you have learned. Start with:
- Working with real-world datasets to build and evaluate models
- Participating in ML competitions (e.g., Kaggle) to gain experience and improve your skills
- Experimenting with different algorithms to see how they perform on various types of data
- Taking part in group projects or coding challenges to apply what you’ve learned in a collaborative setting
The more you practice, the more confident you’ll become in your ML abilities.
3. Overfitting and Underfitting Models
When first learning machine learning, many beginners face two common issues: overfitting and underfitting. Overfitting occurs when a model learns the training data too well. The model captures noise or random fluctuations rather than the underlying pattern and will perform well on the training set and poorly on new data. This is a typical beginner mistake! In contrast, underfitting occurs when the model is too simple and cannot capture the true complexity of the data. Underfitting leads to poor performance on the training dataset and the testing dataset.
Why It Happens:
Many beginners make the mistake of concentrating solely on having accurate models of their training data and not considering how the model may perform on new data. Thus, beginners often over fit their models. Underfitting often happens when beginners are using overly simplistic models or not paying enough attention to hyper parameter values.
How to Avoid It:
To avoid both overfitting and underfitting, follow these tips:
- Use cross-validation: Split your data into training and validation sets to get a better sense of how your model will perform on unseen data.
- Choose the right model complexity: Start with simple models and gradually increase complexity if needed. Use regularization techniques like L1 and L2 to prevent overfitting.
- Tune hyper parameters: Experiment with different hyper parameters to optimize your model’s performance.
- Monitor performance on validation data: Always track how your model performs on the validation data (not just the training data) to ensure it generalizes well.
The point is When you train a machine learning model you should be considering the very important trade-off between high accuracy and good generalizability.
4. Ignoring Feature Engineering
Feature engineering refers to the process of selecting, altering, or creating new input variables (features) from raw data. Beginners can often drastically underplay the significance of this step by ignoring their input features, as they rely on the features given in their dataset, which can lead to poor model performance because in order to make the correct predictions, it is very important to have the right features.
Why It Happens:
It’s worth noting that feature engineering can be time-consuming, and you must understand both the data and the problem space extremely well. Beginners may feel that they can get away with using default features or using automated feature selections, but this is arguably the most important part.
How to Avoid It:
Take the time to analyze your dataset and the controls between features. The following are tips to improve your feature engineering:
- Removing irrelevant or highly correlated features that can introduce noise into the model.
- Creating new features that might help improve model performance (e.g., combining columns or transforming data).
- Handling missing data by using imputation techniques or removing rows/columns with missing values.
- Scaling or normalizing features to ensure that they are on similar ranges, especially for algorithms that are sensitive to the scale of the data (e.g., KNN, SVM).
Great feature engineering can be the difference between a mediocre and a high-performing model.
5. Not Continuously Improving and Learning
Machine learning is a rapidly changing field, with new algorithms, new techniques, and new best practices being developed all the time. A common mistake beginners make is that they think that after learning the basics and completing a course, they can stop learning. In truth, being a good machine learning practitioner requires the ability to keep track of what is new and continuously improve skills as a process of lifelong learning.
Why It Happens:
With such a broad topic with complex ideas, it is overwhelming and many beginners feel stuck once they have completed a Machine Learning Course. It is part of a life-long learning process, and now that you have started, the key is to keep being curious and improving.
How to Avoid It:
To keep improving, follow these strategies:
- Read research papers and stay updated on the latest developments in the field.
- Join online communities (e.g., Reddit, Stack Overflow, or LinkedIn groups) to discuss challenges and share insights with others.
- Participate in real-world projects and try to apply new techniques to solve different problems.
- Keep learning: Consider taking advanced courses or specializing in specific areas of ML, such as deep learning, natural language processing (NLP), or reinforcement learning.
Committing to this idea of life-long learning, you can continually improve your ML skills and be ahead.
Frequently Asked Questions (FAQ) on the 5 Mistakes Beginners Make When Learning Machine Learning
Why is skipping the basics of mathematics such a big mistake in machine learning?
Mathematics is essential to most machine learning algorithms and encompasses concepts such as linear algebra, calculus, and statistics. The mathematics explains how machine learning models work and how those models are optimized.
If those starting out in machine learning do not understand the mathematics behind the algorithms, as a beginner they likely will not know how to interpret the output, troubleshoot and fix problems, or refine and improve the models. For example, one cannot even begin to understand gradient descent, which is essential to most machine learning algorithms, without understanding calculus. Therefore, missing this foundational knowledge will make it more difficult to understand how machine learning works in reality, and severely limit a beginner’s capacity to improve performance of models.
Can’t I learn machine learning without a strong theoretical background?
While it can be okay to begin coding and using machine learning models without really understanding the theory, this is not likely to be the best strategy in the long run. By not having a theoretical grounding you will miss key insights about how and why the algorithms work.
As a result, challenges may arise when attempting to troubleshoot issues that arise, optimize your models, or use machine learning in practice to solve a real world problem. Theoretical knowledge will increase your understanding about the strengths and weaknesses of the various models. These insights are important because they will improve your work. A good Machine Learning Course will typically provide both theoretical understanding and practical experience and provide you with the best of both worlds.
How do I avoid overfitting and underfitting my machine learning models?
When a model develops too complex or simple for the dataset, we are in or out of the region of overfitting or underfitting. To keep these in check, I would suggest beginners to use cross-validation before rezoning with anywhere but baselining the model. Cross-validation enables better knowledge of the model’s predictive capabilities, in addition to avoiding chances of overfitting.
Nevertheless, a beginner needs to start building simple baselines for regression and classification, and don’t build more complex models unless you’re going to acknowledge performance evaluation or business metrics in a data set. It is often useful and practical to monitor the model’s performance on training data and validation data. This will have indicated how well the model is generalizing to any.
Next, regularization (L1 or L2) will also try to prevent overfitting, Burnout, perhaps trial and error, or an acknowledgement of a model’s complexity a parameter can solve the over and underfitting problem. I trust the materials, focus your methodologies and in time will aim for predictive outcomes on harnessing training performance as well as promoting unseen testing performance for representative learning across multiple dimensions.
Why is feature engineering so important in machine learning?
Feature engineering is a pivotal element of the data preparation stage which includes developing new features, changing existing features, or picking nice features to use to help machine learning algorithms perform well. The data you get from a raw data source is going to need processing and transforming to make it useful in a machine learning model. Very frequently, beginning practitioners do not appreciate the impact that feature engineering can have, and they just use the default features from a dataset.
The resulting numbers will reflect the poor feature selection and your bad predictive performance. Feature engineering will be able to highlight some of the relationships in the data space that are not readily apparent for even greater predictive results. Feature engineering can include handling missing data as well as presenting data at better or larger scales or aesthetically removing noise. Investing time in feature engineering can yield substantial returns on your model’s predictive performance.
Final Thoughts: Learn the Right Way with Industry-Focused Guidance
Nurturing your machine learning knowledge is a rewarding and complicated journey. Avoiding these common pitfalls can help you sidestep months of frustration and put you on a more productive learning path. Self-studying is an effective method of learning, but if you enroll in a structured, hands-on Machine Learning Course you will find that you can speed up your journey of learning this complex subject, and sometimes, a bit of mentorship goes a long way.
Why Choose a Professional Program?
One machine learning program worth exploring is run by a world-class training provider with a strong, highly-practical project-based approach to the world of job-readiness. The students in this program have dedicated mentorship, real-world live case studies, and an ongoing alumni network which can offer, by the end of the course, the students far more than just theory and knowledge–the students will be job-ready!