Many people are interested in the field of Machine Learning but don’t know how to get started. Here are some dos and don’ts for those who want to learn more about this fascinating area.
The Dos
1. Invest Your Time in Learning
Take your time when it comes to learning about machine learning so you can become an expert with enough knowledge on the subject matter. Mastery of the subject matter is one of the most important things you should have in machine learning.
Do not follow tutorials for projects unless it is a straightforward project. Following too many tutorials will hinder your learning process because you are just following another person’s steps which won’t allow room for growth or exploration on your own.
The best way to learn about machine learning is by actually doing a project of your own. The self-initiative will allow you to figure out what works and doesn’t work for various situations, which helps you learn more about this subject matter.
2. Be Keen on the Data
One of the most important things you should have for machine learning is a robust data set. Having good data while working on projects will help you get better results. It also allows for faster learning processes about this subject matter.
Datasets for machine learning projects should be big enough so the algorithms can learn from them. It would be best if you had a variety of data types because this will allow for better learning and results in your Machine Learning project.
The best way to gather good-quality datasets is by using different sources such as books, websites, and research papers. Always check if there are any licensing issues with the datasets you are using to ensure that they can be used for your project.
3. Be Well-Versed On The Models
The algorithms are the most important part of machine learning. It would help if you were very well-versed in these models to choose which one is best for your project, depending on the data set.
There are many machine learning models, such as regression, clustering, and classification. Some models are better suited for a certain type of data, while others may not be the best choice.
For example, if you have a large dataset with many variables, regression should probably be your first option for algorithms in machine learning. On the other hand, clustering should work well with small datasets with only a few variables.
You should also know about K-Nearest Neighbor, Naïve Bayes Classifier, Support Vector Machines (SVM), Decision Trees, and Random Forests. These are some of the most popular machine learning models used today in various projects. Each model has its pros and cons, so you need to know when best suited for specific situations.
The Don’ts
1. Don’t Specialize
Don’t focus on one type of machine learning because there are so many different types out there. Each type of machine learning has its unique implementation process. By specializing in one type of machine learning, you are cutting yourself off from knowing more about this fascinating subject.
2. Don’t Limit Your Knowledge
Don’t limit yourself to only getting knowledge in machine learning. Expanding your horizons and gaining knowledge in other areas will help solve many problems with machine learning that might not be present if you were to specialize in the topic.
3. Don’t Use Small Datasets
Do not use small data sets such as less than 100 samples or features since this will give abnormal results. Most algorithms won’t run on them due to their size. The latter is why having a large dataset is very important when working on machine learning projects.
4. Don’t Skimp On The Algorithm Features And Specifications
Don’t skimp out on the algorithm features because you feel like it might be too much work. Skimping is a common mistake when people are just starting to learn about machine learning and then realize that there’s more to this subject matter than what meets the eye. Having good-quality and relevant features will help you get better results from your machine learning projects.
In summary, by actually doing a project of your own, you will figure out what works and doesn’t work for various situations. Self-initiative helps you learn more about this subject matter.
