Learning Machine Learning is no easy task. However, with it being such a dynamic industry with a wide range of opportunities now and into the foreseeable future, it is a sector definitely worth investing your time and energy into.
We have looked at some of the best online Machine Learning courses currently available here.
We have also discussed the growth prospects of the industry and the advantages of taking a course, here.
So, assuming you are ready to make the leap to begin learning machine learning, how can you make the learning curve less daunting?
The following guidelines on important concepts should help.
If you read up on the various points below and form a rudimentary understanding of what they are and how they are relevant to machine learning, before you take a course, you will be well on your way to a more rewarding learning experience.
Essentially, to get the best possible start on your machine learning journey, this is where you should begin.
Table of Contents
What is Machine Learning (ML)?
First, of course, you need to know what machine learning is. ML is the process of enabling computer systems to learn, progress and develop utilizing imputed data rather than a coded program.
The computer actively engages with an algorithm/s so that it is able to learn from data and make predictions on that data.
Because data is such an important “ingredient” to machine learning, the sector is understandably related to mathematical optimization, predictive analysis, statistics, data management, and collation.
The Two Types of Computational Machine Learning
Machine learning can be split into two distinct types:
Supervised Machine Learning
Supervised machine learning is where the computer is presented with example data (classed as inputs). This data forms the basis on which a set of desired outputs are formed.
Once the input, output relationship has been introduced to the computer, it learns to apply those general rules to incoming data batches. It will then create an appropriate output.
In other words, supervised machine learning is the process of teaching a computer how to convert inputs into outputs.
Unsupervised Machine learning
Unsupervised machine learning is where computational power really comes into its own.
In this type of machine learning, there are no labels given to the learning algorithms for the desired output.,
In other words, the computer is made to discover patterns within the data on its own, and within that structure, it will produce an output.
This process is classed as feature learning, which in layman’s terms can be seen as “discovering a means toward an end”.
- Related Content: A Short Introduction to Machine Learning
Which Direction in Machine Learning do you want to take?
In order to learn machine learning in an efficient way, you may need to ascertain which direction you wish to go in this exciting sector.
If you are more interested in the theory behind the algorithms and how they work, you will need to be comfortable with statistics, mathematics, and probability variables. Linear algebra and calculus are essential in this regard.
If your focus is to write and implement the algorithms, you should focus your studies on machine learning alongside Python programming.
However, nothing actually beats knowledge on both theoretical mathematics and practical application.
Let’s now take a look at some of the more important machine learning skills and concepts.
- Related Content: 5 Top Tips when Learning Python Programming Online
Skills Required before Learning Machine Learning
The following bullet points are key skills that anyone looking to learn machine learning should be comfortable with:
- Linear algebra
- Probability theory
- Computer Programming (Python, R Programming, Julia)
- Optimization theory
Key Concepts involved with Machine Learning
The following are some example concepts you will be introduced to when learning machine learning.
Multivariate querying is the method of finding similar objects within a dataset.
- Range search
- Nearest neighbors
- Farthest neighbors
The computer will learn to analyze data in order to find relationships such as this.
Regression focuses on the estimation of continuous or numerical variables.
Example estimations in a practical sense could be the future housing prices of a specific area, a company stock price, and retail prices can be estimated using regression machine learning processes.
As this can be a complicated, yet highly valuable machine learning process, there are several methods for estimation using regression.
- Support vector regression
- Regression trees
- Linear regression
- Kernel regression
- Gaussian process regression
Dimension reduction is the process where the reduction of the number of random variables is divided into feature extraction and feature selection.
Dimension reduction-related problem is normally solved using the following methods:
- Principal component analysis
- Manifold learning/KPCA
- Non-negative matrix factorization
- Compressed sensing
- Gaussian graphical models
- Independent component analysis
Clustering is where the computer will group data and in order to discover labels that can be associated with each of the group datasets.
This lends itself to the analysis of customer behavior and segmentation as well as feature identification, (products, trends, etc)
Clustering methods include:
Classification is where the computer is expected to predict variables and/or categories of data.
This type of machine learning can be found in the algorithm that controls your spam folder for example. It is also used to detect transaction fraud within the banking system.
Classification methods in machine learning include:
- Random forests
- Deep learning
- Logistic regression
- Kernel discriminant analysis
- Naive Bayes.
- Decision trees
- Artificial neural networks (ANN)
- Support vector machine (SVM)
- K-nearest neighbors
- Boosted trees
And there you have it, a nice long list of things to start reading up on so that when you take your machine learning course, you go straight to the top of the class and hit the ground running. Good luck, and have fun with it. You have a bright future ahead of you…