The Top 10 Machine Learning Algorithms Every Beginner Should Know!
These algorithms can help you learn how to use machine learning.
In the realm of machine learning, we encounter the intriguing “No Free Lunch” theorem. In essence, this theorem underscores the reality that no single machine learning algorithm can reign supreme across all possible problems. This notion holds especially true in the context of supervised learning, where the main objective is to create predictive models.
In simpler terms, claiming that a particular algorithm — let’s say neural networks — will always outperform others like decision trees, is an oversimplification. The interplay of diverse factors, including the nuances of the dataset’s size and structure, comes into play. This dynamic signifies that the efficacy of any algorithm hinges on the unique characteristics of the specific problem at hand. As a result, you should try many different algorithms for your problem, while using a hold-out “test set” of data to evaluate performance and select the winner.
Of course, the algorithms you try must be appropriate for your problem, which is where picking the right machine-learning task comes in. As an analogy, if you need to clean your house, you might use a vacuum, a broom, or a mop, but you wouldn’t bust out a shovel and start digging.
THE BIG PRINCIPLE BEHIND MACHINE LEARNING ALGORITHMS
However, there is a common principle that underlies all supervised machine learning algorithms for predictive modeling.
Machine learning algorithms are described as learning a target function (f) that best maps input variables (X) to an output variable (Y): Y = f(X)
This is a general learning task where we would like to make predictions in the future (Y) given new examples of input variables (X). We don’t know what the function (f) looks like or its form. If we did, we would use it directly and we would not need to learn it from data using machine learning algorithms.
The most common type of machine learning is to learn the mapping Y = f(X) to make predictions of Y for new X. This is called predictive modeling or predictive analytics and our goal is to make the most accurate predictions possible.
Most Common Machine Learning Algorithms
For machine learning newbies who are eager to understand the basics and to grasp the fundamentals of machine learning, here’s a rapid overview of the ten key machine learning algorithms frequently employed by data scientists.
- Linear Regression
- Logistic Regression
- Linear Discriminant Analysis
- Classification and Regression Trees
- Naive Bayes
- K-Nearest Neighbors (KNN)
- Learning Vector Quantization (LVQ)
- Support Vector Machines (SVM)
- Random Forest
- Boosting
- AdaBoost
So, let me throw this question right back at you: do these algo’s ring a bell, or are they venturing into uncharted territory? If it’s the latter, well, it seems like there’s a whole world of discovery waiting for you. But fret not! Rest assured, I’m considering treating you to a simplified overview of all these algorithms on a different day. Until then, here’s to an enriching journey of learning! Cheers!
