We produce a lot of data daily. Most of this data is not analyzed despite being useful because it is impossible for humans to analyze so much data. Hence, the need for machines to learn how to make sense of data. This blog provides insights into machine learning prerequisites. But, before that:
What is Machine Learning?
Machine learning refers to a computer’s ability to learn from data without necessarily being programmed to do so. It’s a field that combines computer science with statistics to make the use of data meaningful.
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Supervised vs Unsupervised Machine Learning
Machines learn predominantly in two ways – supervised and unsupervised. In supervised learning, machines use a sample set to predict outputs for inputs. Unsupervised learning, on the other hand, requires no sample set. Computers detect hidden patterns or trends in data sets because of their efficient ways of observing them.
Demand for Machine Learning
Machine learning is huge across the globe with salaries soaring. Entry-level salaries for data scientists with machine learning skills generally range between $ 100,000-150,000. And with data being used for all sorts of purposes across industries, the demand is only expected to increase.
Sure, machine learning won’t help you crack the stock market or make you rich in a day. But, it’s a fun and exciting field to work in that offers plenty of opportunities for quick career growth.
Machine Learning Prerequisites
So, what are machine learning prerequisites?
Firstly, you don’t have to know everything in statistics or programming to start your machine learning journey. But you must know enough to be able to apply concepts from both these fields to data and make it useful. You also need a fair understanding of mathematics. Let’s explore these machine learning prerequisites further.
When it comes to programming languages, Python seems to come out on top for data science purposes. In fact, it is one of the best languages for anyone interested in programming. Python helps you in data wrangling, building predictive models, data visualization and more. It is used across the globe and recorded one of the highest growths in demand.
The best feature? It’s simple. Many believe it is the ideal first language for a programmer as it achieves most tasks with less code. It allows you to implement solutions faster and saves you time in the process. Plus, the language has dedicated libraries for data analysis and machine learning.
Python has a vibrant data science community that offers plenty of videos to learn from. The community also shares bits of codes or solutions to common problems. Stack Overflow is another good source to brush up your Python skills or even learn it. Do you need a computer science degree to learn Python? The short answer is no.
All you need to understand is the underlying logic to make choices between functions or conditional statements, etc. No need to remember syntax. Use a top-down that is result-oriented and starts with the core concepts. Improve your knowledge of concepts as you go along and get plenty of real-world practice (Kaggle, DIY, collaborate with mentors) in the process. This will help you hone your skills better.
Focus also on data science libraries. Libraries save time by letting you import solutions. Get familiar with the Jupyter Notebook, which is a darling of data scientists. Then there is NumPy, which is great for numerics. Pandas for data structures and exploratory analysis. Matplotlib lets you plot data and visualize it. Scikit-Learn is a machine learning library with algorithms and modules that suit pre-processing, cross-validation, etc.
Statistics & Probability Theory
It may seem obvious that data science and machine learning relies on statistics – a field that specializes in collecting and analyzing data to garner useful insights. Statistical concepts like hypothesis testing, significance, regression, etc., and probability theory will help you make key business decisions. Regression modelling is used, for instance, in retail to maintain an efficient supply chain that is neither under-stocked or over-stocked as both scenarios are detrimental for any organization.
Machine learning particularly requires a Bayesian way of thinking and adapting to any additional information that may go against existing knowledge and beliefs. Bayesian thinking includes concepts like conditional probability, maximum likelihood, priors and posteriors. It is different from the frequentist way of thinking, which only assigns probabilities to collected data. In Bayesian thinking, probabilities are assigned before and after collecting data. The phrases for these two sets of probabilities are prior probability and posterior probabilities.
The data that machine learning models use have a significant impact on the output you get or the efficiency and accuracy of your model in dealing with the data. Hence, it is important that you know how to determine the underlying assumptions of your data set to transform it and make it bias-free and more reliable. This requires knowledge of probability distributions and the ability to test hypotheses effectively to improve your models. Many of the decisions that you will make in machine learning will require a good understanding of statistics and probability theory.
The best way to learn how to use your understanding to improve decision-making is by getting hands-on, real-world practical experience. Theory is great, but nothing compares to the joy of sinking your feet into problems and solving them.
The role of mathematics in machine learning varies from case to case and your job role really. At the entry-level, you don’t need a lot of it because there are algorithms and libraries of codes that you can use without knowing basic linear algebra or multivariable calculus.
Nonetheless, as you progress in your career and increase your capacity, you will want to customize or build your own machine learning algorithms. And these, will require a better understanding of mathematics. Roles that are heavy on research or that require you to implement algorithms by transferring them from academic papers to a code that works, will also need linear algebra and multi-variable calculus.
Eigenvalues and logistic regression are useful in principal component analysis, for instance. Linear algebra is also useful in machine learning because they enable you to represent voluminous data with multiple variables in matrices. Calculus, on the other hand, is useful in calculating derivatives. Gradient descent is also a popular optimization technique.
So, there you have it – machine learning prerequisites. To recap, you need a fair understanding of statistics, programming and mathematics. If you’re serious about pursuing machine learning, you will also need plenty of hands-on practical experience on real applications and problem-solving. You can do this in many ways, including by signing on with a mentor, as I have noted. So, get started and cover all the machine learning prerequisites. We wish you a happy machine learning experience!