How Difficult Is Machine Learning?

Although many of the advanced machine learning tools are hard to use and require a great deal of sophisticated knowledge in advanced mathematics, statistics, and software engineering, beginners can do a lot with the basics, which are widely accessible.

A variety of supervised and unsupervised learning models are implemented in R and Python, which are freely available and straightforward to set up on your own computer, and even simple models like linear or logistic regression can be used to perform interesting and important machine learning tasks.

To master machine learning, some math is mandatory. Linear algebra, statistics, and probability form the foundation of machine learning. If you have serious plans to join the machine learning bandwagon, it’s time to brush up on your high school math.

Apart from math, data analysis is the essential skill for machine learning. The ability to crunch data to derive useful insights and patterns form the foundation of machine learning. Loading a large data set, cleansing it to fill missing data, and slicing and dicing the data set to find patterns and correlation are the critical steps in data analysis.

Even if you’re not the type of person who can instantly understand histograms, bar charts, line charts, and pie charts, you need to appreciate the power of visualization. Spend some time with Microsoft Excel to understand Pivot Tables and various visualization techniques available as charts.

When it comes to machine learning, a little knowledge goes a long way. A variety of supervised and unsupervised learning models are implemented in R and Python, which are freely available and straightforward to set up on your own computer, and even simple models like linear or logistic regression can be used to perform interesting and important machine learning tasks.

We should mention that many of the more advanced tools do require deep knowledge of advanced mathematics, statistics, and software engineering. Some key skills include an understanding of probability and statistics, complex linear algebra, and calculus in order to grasp the fundamentals of machine learning and easily work with data matrices.

If you’re going to pursue machine learning, it’s a good idea to start with these key mathematical concepts and move onto the coding aspects from there. Many of the languages associated with artificial intelligence such as Python are considered relatively easy.

For those of you already strong with math, the next step is picking the right machine learning framework. Essentially, there are a plethora of libraries to choose from when building out your model such as NumPy, Scikit-Learn, and Pandas. These toolkits range in difficulty depending on how advanced the project is. Find one simple framework to start with and grow from there.