Machine Learning for Founders: Practical Guide to Kick-Off

Machine learning can be complicated, especially for founders. Below, we dive into all the details you need to know about machine learning to make implementing machine learning much easier for your entire team. 

What Is Machine Learning?

Machine learning is a computer science discipline dedicated to creating algorithms that allow computer systems to analyse data and make predictions or decisions without being explicitly programmed for that specific task.

To accomplish this, developers must create machine learning algorithms that account for all the variables the computer system must consider before making decisions or predictions.

Basically, this discipline uses statistical and mathematical models to perform tasks and adjust based on feedback. The models are supposed to learn from the feedback, allowing the machine learning model to produce more consistent and reliable results.

Founders can apply machine learning models and algorithms to various fields and products. It's ideal for identifying complex patterns, giving recommendations, and even making autonomous decisions.

Machine learning plays a major role in the development of artificial intelligence, and it's slowly becoming a mainstay in modern technology, which is why it's something founders should consider when developing an app.

Why Machine Learning Is an Integral Part of Modern Software Solutions

Machine learning is very important for modern technology and society. These data-driven models allow for better and more accurate decision-making while making unlocking valuable insights much easier. This is because machine learning algorithms are designed to intake and process large amounts of data at a time.

This is why a well-developed machine-learning model provides founders with great insights while streamlining the app's overall functionality. By training algorithms to make better decisions based on certain feedback, data scientists can create very powerful models that identify trends that usually go unnoticed.

Additionally, machine learning technology can improve the efficiency and functionality of various platforms. Machine learning algorithms use the training data sets to make decisions or predictions on the app. This allows you to fully automate certain processes, which is especially beneficial for repetitive processes. That way, you get to reduce human error while boosting the entire app's functionality.

Types of Machine Learning

There are various types of machine learning that you need to understand as a founder. Each machine learning algorithm and approach has its benefits and drawbacks, but choosing the best one for your product is important.

The types of machine learning include:

  • Supervised machine learning: This model learns from labelled examples and uses the given input and corresponding output to make decisions based on the data it receives.
  • Unsupervised machine learning: Unsupervised learning includes training data with unlabeled examples. This approach provides only the input, allowing the algorithm to learn patterns, structures, and more.
  • Reinforcement learning: This type of machine learning uses a trial-and-error approach to develop the algorithm. There are rewards and penalties for each action of the algorithm, which trains the data and model to respond to how the developer plans it.
  • Deep learning: Deep machine learning focuses on artificial intelligence and neural networks that have multiple layers. This approach leads to accurate data predictions and decisions due to the large amount of data that these algorithms can process.

The type of machine learning model you choose will greatly impact your product's functionality and effectiveness. So, make sure to take your time figuring out the best model for your needs.

What Is the Best Language for Machine Learning?

There is no best language when it comes to machine learning. Each machine learning developer tweaks the language and approaches they use based on the project. That way, they can develop algorithms tailored to the founder's needs, creating a more functional and efficient final product.

While the best language for your machine learning app depends on personal preferences, app requirements, and many other factors, there are many common languages that developers use when developing machine learning models, including:

  • Python: Python is one of the most versatile and popular languages for a machine learning system. This works with artificial neural networks, supervised learning algorithms, and more. This is why machine-learning engineers often use this language for their machine-learning programs.
  • JavaScript/Typescript: this is not only the leading web development language for both front-end and back-end, but also fully supported language by the leading machine learning development solutions and tools like Tensorflow, Huggingface Solutions, PyTorch.
  • R: R is a language that many machine learning engineers use when developing programs for statistical analysis, which is why it works great for reinforcement learning, a supervised learning algorithm, and more.
  • Java: This is a general-purpose language, which means it has many applications, including neural networks, artificial intelligence, and machine learning. This language has one of the steeper learning curves for machine learning engineers, but it offers great versatility, which is why it's a common language for machine learning systems.

With all the current coding languages available, it's impossible to single out one as the best. This is why you need to determine which language best suits your project's needs. Each language has its drawbacks and benefits, and it's up to the founder to figure out which one fits the project.

Common Misunderstandings About Machine Learning

There are many common misunderstandings about machine learning, but the most common one is that it’s a mystical solution that automatically solves any problem. With how machine learning is portrayed in the media, it’s easy to see why many people land on this oversimplified assumption.

To clear things up, here are some things you need to know about machine learning.

Machine Learning Needs Human Help

While machine learning algorithms learn trends and make predictions based on data, they need to be set up, trained, and evaluated by a human developer. Many of the foundational elements of a machine learning algorithm, such as data gathering, preprocessing, feature engineering, and model selection, must be performed by a human. This is why machine learning algorithms cannot exist without proper human development.

Data Quality Is Crucial

The accuracy and effectiveness of machine learning models depend on the quality of the data being fed. It’s important that the machine learning models receive good and unbiased data to give accurate impartial predictions. This is why it’s crucial for teams to clean, preprocess, and validate data before feeding it into a machine-learning model.

Machine Learning Isn’t a Cure-All

Don’t get us wrong; machine learning is a very powerful tool that can handle a lot of your programs’ problems and issues. However, this isn’t a one-size fits all answer to your product’s issues. There are certain jobs and domains within your program that may not be suited to machine learning or may actually affect the machine learning model’s effectiveness. This is why you need to understand your problem, explore different solutions, and then decide whether a machine-learning model is the right approach for your organisation.

Interpretability & Explainability

Machine learning models can be incredibly hard to understand, which is why some of them are called “black boxes.” This is why many people worry about how well these models can be explained and how easily people can understand the concept. While interpretable machine learning methods are improving, this is the expectation, not the norm. So, make sure that you have this in the back of your mind when using machine learning for important or crucial areas of your program.

Ethical Considerations

Machine learning can repeat the biases present in the data that they were trained on. This is why you must consider the ethics, justice, and potential biases in the data that you feed machine learning systems. If you feed biassed data into the model, you will receive biassed predictions. On top of that, founders must ensure that they use their machine-learning systems responsibly to avoid causing harm and discrimination.

How to Find a Suitable Machine Learning Developer for Your Project

Every machine learning developer has their own strengths, weaknesses, and skills. Your app's needs when it comes to machine learning will help determine the best engineer for your project. When you lay out the requirements, it will be much easier to see which approach will work best: supervised learning, unsupervised learning, or even clustering algorithms and neural networks.

From there, you can look at the market to find the right machine learning engineer for the job. When interviewing different candidates, always refer back to your product's needs. That way, you can clearly see whether or not certain candidates have what it takes for the job.

There's no harm in taking the time to find the best developer for your product. This is because there are many things to consider when developing machine learning models, and it will take the right engineer to take your app to where it needs to be while also meeting your goals.

When you interview candidates, consider their technical ability and compatibility. Whether you're creating a deep learning or supervised learning model, your developer must have the technical skills necessary for the algorithm. However, it's also crucial that your developer can develop a healthy and safe working relationship with your team for a much smoother development process.

Essential Skill Sets Every Machine Learning Developer Must Possess

It's paramount for your machine learning developer to have a diverse range of skills, particularly when aiming to develop an impactful and seamless operational model.

The specific skills necessary for the job vary from project to project, but some of the things you should definitely look out for include:

  • Programming Language Knowledge: Machine Learning is very complex and requires knowledge of various developing languages. That way, the engineer can choose the ideal language for your project's needs and effectively apply it in their natural language processing or other tasks.
  • Machine Learning Experience: It's important that your engineer understands the world of machine and deep learning. This usually requires them to have spent many years in the field, which means they have a lot of hands-on experience with different algorithms. That way, finding the deep learning model that's best for your project will be much easier.
  • Mathematics and Statistics: This branch of computer science deals with many mathematical and statistical data. It's important for your engineer to easily understand, tabulate, and analyse these data points so that they can tweak your model to serve your app's needs.
  • Problem-Solving: There's a high chance that your team will run into roadblocks and development issues when creating your product. This is why machine learning engineers must possess good problem-solving and critical-thinking skills to make it easier.

Each machine learning project requires a different skill set. However, it's important that the engineer for your project possesses these basic skills to get the job done.

How to Evaluate a Machine Learning Developer's CV

There's a high chance that you'll interview and consider multiple candidates when finding an engineer for your neural networks. It's important that you evaluate their CV properly to gauge how well they can input data, and whether or not they can develop your artificial neural network.

When candidates hand you a CV, one of the first things to look at is their educational backgrounds and qualifications. You should look at which machine learning techniques they understand, where they learned how to develop supervised and unsupervised learning algorithms and the different fields they have worked in. That way, you get a general idea of their technical skills and ability.

It's also recommended to consider the projects that they've worked on before and what machine learning methods they have used. Remember, machine learning algorithms are complex systems, and it's important for your engineer to understand how to develop machine learning platforms effectively.

Another thing to consider when viewing a CV is the candidate's dedication to continuous learning. Try to see if they've attended any classes or workshops in recent years, as this shows whether or not the developers take continuous learning and improvement seriously.

How to Evaluate a Developer's Portfolio

A developer's portfolio tells you a lot about their skills and experience. Again, many machine learning applications are out there, so developers usually have varied skills and experiences. That's why you need to pay close attention to your developer's portfolio, as it gives you a lot of information, especially if you're determining whether or not they are the right choice for your project.

First, you need to determine whether their project history aligns with your needs. Again, data mining methods and the approach to your machine learning model largely depend on your platform's needs and the type of project. So, try to see if the developer has worked on projects with similar needs or requirements to yours, as this will make it easier to determine if they are a good option for your team.

Additionally, try to look at the level of complexity of their project history. There are many complex tasks that developers need to perform when building a machine learning algorithm, and it's important that they have the experience to handle them.

Lastly, try to look at the actual portfolio they submitted. Is it laid out properly? Can you clearly see the data and project history? If so, this shows a certain level of professionalism, which is very important when hiring machine learning engineers.

Ace the Final Interview: Expert Tips for Meeting the Ideal Candidate

Everyone’s team has a different approach to finding the best data scientist and developer for their machine learning needs. Again, every project has unique needs and requirements, so one team may look for certain qualities in their candidates, while another may look for completely different qualities.

It's very important to tweak your selection process based on your project's needs so you can easily find the perfect addition to your development team. To make it easier for you to choose the best candidate for your needs, here are a few key tips to keep in mind during the final interview:

  • Prep the interview questions: Machine learning is complicated, so you must prepare the questions you want to ask the engineer beforehand. That way, you can stay consistent during interviews and learn if the candidate has the qualities you need for the project.
  • Give them a chance to talk: The final interview is your chance to learn more about the candidate. This is why you shouldn't cut them off or give them questions that merit short answers. You need to learn as much as possible about candidates before bringing them on the team, which is why you should feel free to let the candidate talk as much as they need to.
  • Discuss the project: To learn if a candidate can handle the task, you need to explain the project's scope and requirements. Again, you should be as open as possible with project details so the candidate can assess whether or not they have the skills for the job.

When interviewing your candidates, take your time to really get to know them. Remember, the final interview is your last chance to choose the best candidate for a project, so it shouldn't be rushed.