Machine learning may seem like an area with an insurmountable jargon. Such terms as gradient descent convolutional neural networks and support vector machines are tossed around in a way that most people do not understand because it requires a deep mathematical background. However, the basic concepts of machine learning algorithms are, in fact, not that difficult to understand as long as somebody explains them in a straightforward manner.
Learning machine learning algorithms as a newcomer implies beginning with the concepts and worrying about the mathematics only once these concepts have been mastered. As soon as you figure out what some algorithm is attempting to accomplish the implementation and the mathematics to accomplish it become much easier to conceptualize.
What actually a Machine Learning Algorithm does.
In its simplest form a machine learning algorithm is an approach to discovering patterns in data. You provide the algorithm with examples and the algorithm learns based on the examples provided to make decisions or predictions based on the new data that the algorithm has never seen before.
This is the key distinction between the conventional programming and machine learning. In traditional programming you program explicit rules which a computer obeys. Machine learning is the process whereby you feed data and the algorithm determines the rules on its own. The algorithm gains experience in the same way as humans do, but in a highly limited and mathematical manner.
Your data should be of high quality so that the quality of your predictions is high as well. The algorithm you use needs to be appropriate and how well you configure and train the algorithm. Knowing what algorithm to apply in what context is one of the essential skills in applied machine learning.
Linear Regression: The Introduction.
The simplest and most fundamental machine learning algorithm is linear regression and it is the appropriate place to start. It is a modeling of the relationship between a continuous outcome variable and one or more input variables by estimating a straight line through the data.
One of the classic problems of linear regression is to predict the price of houses based on their size in square feet. The algorithm uses previous information to learn what the size vs price relationship looks like and uses the relationship learned to predict the price of new houses.
Linear regression is effective when the correlation between inputs and outputs is actually linear and when you need to obtain a result which can be explained. Its simplicity is its strength, as well as its limitation.
Logistic Regression: It is, In a Way, Regression.
Although it is called logistic regression it is in fact a classification algorithm, rather than a regression algorithm. It forecasts the category that something belongs to as opposed to forecasting a real number. Is this an email spam or not spam? Will this customer churn or remain? Will this tumor be malignant or benign?
The results of logistic regression give a probability between 0 and 1 which is then used to give a category. It is rapid to interpret and has been shown to work very well on many binary classification problems making it a standard algorithm to which more complex models are often compared.
Decision Trees: Powerful and Intuitive.
Decision trees are algorithms which model decisions as a sequence of if-then rules which are organized into a tree structure. The algorithm begins with the root of the tree, a series of yes or no questions are asked about the input data and the algorithm follows the branches based on the answer provided until it reaches a prediction at the leaf nodes.
Decision trees are very interpretable – you can trace the route that a decision takes and understand very clearly the reason why the model made the prediction that it did. This interpretability is highly desirable in such areas as medicine or finance where decisions must be interpretable.
Single decision trees have a weakness: they overfit easily, that is, they will memorize the training data, and not learn generalizable patterns. Random forests solve this by using a large number of decision trees and averaging the results of the decision trees to come up with a lot more robust results.
Neural Networks: the Foundations of Deep Learning.
Neural networks are vaguely based on the organization of the human brain. They are made up of layers of interconnected nodes that process and transform input data by a sequence of mathematical operations. Deep learning is a type of neural network that has numerous layers – the depth of the neural network can be expressed in terms of layers.
Neural networks are best suited to problems that require images, text and audio and the corresponding patterns are too complicated and cannot be expressed through straightforward rules. Image recognition Image recognition is a natural language processing and voice assistants, all of which are based on deep learning methods.
Trade-offs are that neural networks take large amounts of training data, which is significant in terms of computational resources and generates results that may be hard to interpret. They are potent tools, but not necessarily the appropriate tool to apply to each problem.
Selecting the appropriate Algorithms.
Among the most viable machine learning skills is understanding what algorithm to use on what problem. There are some guiding questions that assist in framing the choice. Is your outcome variable categorical or continuous? What is the amount of data you have? What is the degree of importance of interpretability? What are your computing limitations?
In the case of most of the beginners, the logical choice is to start with the linear or logistic regression and decision trees. They are well understood and can quickly execute their algorithms and provide a solid performance base that more complex algorithms must overcome before it is worth the extra complexity.
Final Thought
Machine learning beginners Machine learning algorithms are so much easier to understand, when you know what each one is attempting to achieve and why. You do not have to be able to master the mathematical proofs before you can begin to be useful- you need a clear mental model of how each algorithm works and when it is applicable. Temper that base with the more basic algorithms practice in applying the algorithms to real data and then allow the higher level of understanding to emerge naturally there.
FAQs
Q: What is the easiest machine learning algorithm for beginners? A: Linear regression is the most approachable starting point as it is conceptually simple and mathematically accessible.
Q: Do I need to know advanced mathematics to learn machine learning? A: Basic statistics and linear algebra are helpful but many beginners start with practical implementation before diving deep into the math.
Q: What programming language is best for machine learning? A: Python is the dominant language for machine learning with libraries like scikit-learn TensorFlow and PyTorch being widely used.
Q: What is the difference between machine learning and deep learning? A: Deep learning is a subset of machine learning that specifically uses neural networks with many layers. Not all machine learning involves deep learning.
Q: How much data do I need to train a machine learning model? A: It depends on the algorithm and problem. Simple algorithms can work with hundreds of examples. Deep learning typically requires thousands to millions.
