Is Coding Required for Machine Learning

Machine learning is booming nowadays. It’s hard to find an area where it’s not applicable nowadays. People from different domains are jumping into machine learning, and a lot of this workforce is interested in the mathematics and statistics involved. However, the major obstacle in their way is the programming required to implement machine learning models.

In this article, we will discuss the prerequisites of machine learning when it comes to the coding part. We’ll look at what level of coding expertise one requires if one is keen on becoming an ML engineer. So, sit back and let’s see if coding is required in machine learning or it’s all just a myth.

Is Coding Required for Machine Learning
Photo by Markus Winkler from Pexels

What is Machine Learning?

Machine learning is an application of Artificial Intelligence. It learns from the provided data by finding patterns in it, allowing any new data to be automatically classified, with minimal or no human intervention. Machine learning uses a large amount of data to train and predict new values. 

Machine learning is everywhere and has applications in almost every field of life such as:

  • Self-driving cars
  • Speech recognition
  • Fraud detection
  • Recommendations of products
  • Detection of malware threat

Types of Machine learning 

Machine learning is often categorized into three approaches such as:

  • Supervised learning: The system is exposed to a massive amount of labeled data like images of numbers to train the machine, which would help them to recognize the clusters and differentiate between numbers.
  • Unsupervised learning: The algorithms will help identify patterns in data and categorize them into clusters based on the similarities between the data, e.g., Airbnb clusters houses available for rent by their owners.
  • Reinforcement learning refers to the training of models to make decisions to maximize output in particular scenarios. The training of the model relies on input, and the model will return an output on which the user decides to reward or punish the model.

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Is Coding required for Machine Learning?

Yes, coding is an integral part of machine learning as it enables the training, testing, and evaluation of the machine learning models and implementing them on computer systems. Coding is the only means to communicate with computers and get them to do specific tasks, so it is necessary to program the ML algorithms.

However, once the machine learning model is done training and is delivering accurate results, there is little to no need for coding after that. The models can be controlled through intuitive dashboards and unless you’re looking to further increase the accuracy or tweak the model, you don’t need to dive into coding again.

How Much Coding Do You Need in ML?

Machine learning mostly involves the use of tools and libraries so hardcore coding is not necessary. Moreover, the languages recommended for ML such as R and Python are not very syntax-aggressive so learning them with the sole purpose of ML is pretty quick.

We’ve already established the fact that you don’t need to be an expert in programming to become an ML engineer; a novice who wants to learn machine learning requires only the basic knowledge of the underlying concepts and semantics of the programming language. 

Most machine learning algorithms are complex in terms of implementation and often involve a high statistical knowledge to implement, such as Naïve Bayes, Gradient Descent, and so on. What’s important is to know the concepts behind them so you can use them as efficiently as possible. Programming them comes as a secondary skill here.

Again, this is only if you’re implementing them from scratch, which isn’t what most ML engineers do. Here are a few libraries you could easily use to avoid coding everything from scratch and rather focus on optimization and accuracy: 

  • NumPy: It’s a linear algebra library that helps perform mathematical calculations and it is also useful while working with multidimensional arrays and converting complex and massive data into data frames.
  • Scikit-learn: This library contains an implementation of a wide range of ML algorithms including regression, clustering, and classification. All you have to do is import the required package and start training the data.
  • Keras: It’s a deep learning library to implement neural networks.
  • TensorFlow: It is also a library for machine learning that particularly focuses on training deep neural networks.

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How Far Can I Go Without Coding in Machine Learning?

We’re living in the 21st century and there are alternatives to almost everything nowadays, thanks to the fast-paced technological advancements. Luckily, there is some advanced software out there built specifically for the people interested in applying ML without writing a single line of code.

Sure, these software won’t help you become a top-notch ML Engineer and your abilities will be restricted in more than one place, such as the ability to customize the models as you require, they’re still great for a start. Once you find yourself getting better at problem-solving and model training, you could try your luck on programming as well and uplift your career.

Let’s see a list of software that makes Machine learning possible for non-coders.

AutoML

As the name suggests, the goal of AutoML is to make machine learning accessible to everyone, including the non-experienced coders. There is a very minimal intervention of coding required here and even a beginner can get up and running with his first ML model using this.

Moreover, AutoML helps to handle most of the ML pipeline by itself, which would otherwise take a whole lot of time. For example, it can manage hyperparameters tuning, iterative modeling, model evaluation, and so on. So, even if you’re an efficient coder but want to save your time, AutoML could be your way to go.

Weka

Built for doing machine learning in JAVA, Weka is a great option when it comes to graphical machine learning that requires no knowledge of coding whatsoever. It’s free-to-use software that provides a large bundle of tools that could be used in the ML pipeline.

All you need to do is upload your dataset and straightaway start processing it in mere clicks. Everything will be getting done in the backend and all you need to do is focus on the strategy of your pipeline, rather than worrying about coding and fixing syntax errors.

Orange

Last but not the least, Orange is another graphical Machine learning toolkit that offers a wide range of machine learning, data mining, and data visualization tools. Its interactive nature not only makes it intuitive and easy to use but also very interesting.

Even if you’re a beginner in coding but have good knowledge about how ML works, you can use this to complete your ML tasks in a very efficient manner.

Other Important Skills Required in Machine Learning

Now, let’s discuss what are the skills or prerequisites required for an individual to become a machine learning engineer.

Probability and Statistics

Many machine learning algorithms use functions and techniques of probability such as Bayes rules, conditional probability, likelihood, sampling, and more to ensure efficient results. To analyze and observe the data to build models and train we use the help of statistics which calculates the mean, mode, and median of the sample data. It also helps to calculate the distribution of a population sample to conclude that whether the sample is normal, binomial, Poisson, or normally distributed.

Linear Algebra

A machine learning engineer must have a sound knowledge of linear algebra and knows how to manipulate with matrices and perform basic calculations such as addition, subtraction, multiplication with scalar, and vector multiplication also to derive the transpose of a matrix with inverse and vector space

Calculus

You must have basic knowledge of calculus to study continuous change, with an overview of differential and integral calculus.

Data Engineer Skills

You must have the knowledge to work with big data and perform manipulations on it using SQL and no-SQL while handling unstructured data.

Programming skills

Having basic programming skills is important for an ML engineer. While they might not need to explicitly code solutions like other software developers, surface-level knowledge comes in handy in a lot of situations and separates the average engineers from the best in the field. Some basic areas to get to know are:

  • Data structures: A sound knowledge of how different data structures such as trees, queues, stacks, graphs can be used to help to deal with specific situations in the development of ML models.
  • Algorithms: Basic concepts of algorithms such as searching, sorting, and their optimization.
  • Complexity:  Ability to deal with complex problems such as Big-O notation, np-complete problems. 
  • OOP (Object-oriented programming): A solid grip on OOP (object-oriented programming) Concepts such as inheritance, objects and polymorphism, and abstraction.

Key Takeaway

Throughout the article, we saw that coding/programming is a must-have skill for machine learning. It is an essential part because without coding we cannot implement machine learning algorithms on computer systems. Nevertheless, it’s not necessary to be an experienced or expert coder as the main focus should be the machine learning pipeline and not the programming.

The latest tools and libraries such as the ones available in Python offer a wide range of built-in models that one can use without needing to build them from scratch. You can get your hands onto these libraries to train a handful of different models without actually needing to get proficient in coding. So, as long as your basics are strong, you’re good to go!

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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