Data Science has become an integral part of any business that’s looking to maximize market potential and commercial value. Machine learning is one of the more widespread and commonly used practices due to the value of its predictions and models, as well as a relatively fair cost of adoption. The tech environment today offer the best chance to hop on the artificial intelligence hype train. If you’ve ever thought about launching an ML project but never did – now is the right time to start, and here’s your starter kit.
We’ve made a list of essential skills, knowledge, technologies, and methods that would help you initiate a functioning project. Utilizing our experience with deploying ML for customers like Amplify, Mabat, Bookimed, and Promo, you’ll get insight into the inner works of commercial data science, as well as get a chance to practice your own skills according to our guidelines. So, first things first – let’s begin with some theory.
What is Machine Learning?
Machine learning is the process of using data in machine algorithm training. Subsequently, these trained algorithms will be able to make predictions to help people optimize decisions. The algorithms are divided into several categories based on the types of learning.
Supervised Learning. These algorithm outputs are already known. The input data has a certain label, usually denoted as the variable Y. These algorithms find solutions in a way that is already formatted.
Some of the most widely used Supervised Learning algorithms are:
- Support Vector Machines
- Linear Regression
- Logistic Regression
- Naive Bayes Classifier
- Linear Discriminant Analysis
- Decision Trees
Unsupervised Learning. The input data is not labeled. That means it does not have the variable Y. These algorithms are used to format the way of finding a solution. Here are some notable examples:
Reinforcement Learning. These algorithms include sequential decision making. After the input data is processed and the decision is made, the output would be taken into account while making next decisions. Unlike the previous two groups, this allows for the process of decision making to not be affected by the outcome decisions. Consider a couple worth your attention:
Now that we are through with ML types, let’s look at some of the essential skills and tools:
- Fundamental knowledge of mathematics
- Experience with one of the following programming languages: Python, Scala, R
- Knowledge of Algorithms
- Understanding of CSV files and databases. Some degree of SQL experience to work with databases would be pretty helpful.
- Knowing your way around servers. When you eventually get into a serious project, your own computer would not be enough
- IDE for writing code in the language of your preference
Tip: You can use our free online course on Data Science and Machine Learning to help you start and accelerate your progress. Aside from an extensive knowledge base, we save you time – the assignments are distributed and checked by a Slack bot.
Armed with the basic premise of what data science is and what kind of skills are required to get your feet wet in a live machine learning project, let’s review the steps that will help you structure your work from start to finish.
Step 1. Brainstorm compelling ideas
Ideas are key, so start your data science project by figuring out just what exactly you’d like to achieve. We suspect this project is going to be a first for you, so think about what interests you most. Draw up some high-level concepts, and once you have a few of those nailed down, go narrow and focus on the most feasible idea. Once you’ve reached a conclusion and the brainstorming part is over – WRITE IT DOWN. We can’t stress this enough. Make a proposal, even though you are technically not in a business setting (not for now, anyway). This way you will solidify your idea, and putting things on paper (or a tool of your choice) on a consistent basis is always a good way to track your deliverables.
Reverting back to the theory, let’s say you want to predict a variable Y based on the values of your input data X. In terms of business, this would translate into an e-commerce use case where you predict whether your customer will make a purchase (var Y) on your website based on his behavior (data X): clicks, time on page, different activity factors, etc.
Step 2. Start Small and Focus
With data science projects, there can be a temptation to go big, especially when you start scraping the possibilities and the implications of it all. A data science beginner should absolutely fight that feeling. Exploring what else is out there beyond the scope of your project can often backfire by causing disruptions with no real return on your investment. Focus is essential and should be maintained from start to finish.
The same approach should not be applied to your data though. When it comes to the minimum data set volume, more often spells better. To get a solid and consistent outcome, the needed data set should be at least 1000 samples long to produce something viable.
Step 3. Test and Execute
Planning for everything is good, but at some point, you’ll have to get into the project execution stage. Commercial data science projects have a two-phase implementation: 1) generating insights and 2) acting on them.You are a beginner, so let’s ignore the latter and focus on the task at hand – testing your hypothesis.
Model the first hypotheses and start testing them. Arguably, the easiest language for programming data science and machine learning is Python. We highly recommend scikitlearn library which helps to implement many of the algorithms that we listed in the sections above. Once you believe you’ve reached the desired outcomes and insights, it is time for the implementation stage.
Step 4. Implement
Let’s have a look at the three most popular ways to implement an ML project:
Create a machine learning API. APIs enable the integration between the work of ML engineers and that of a software developer. This method is widely used because it allows integrating insights generated by machine learning into different parts of the product with no need to insert models in the code.
Record results in a database. This method of implementing ML solutions is used when decision-making takes a lot of time and each of the steps should be recorded. Also, this option is suitable when certain stages of a process are performed on different server workers and results cannot be kept in memory. If fragmented, those findings would influence the outcome of the process in the future.
Embed the code. You can do this for your product, website, or application. Embedding code is useful when time matters. Although it is much quicker than creating application programming interfaces, users prefer the latter because creating a machine learning API is more convenient and secure for further project development and iteration. If you are looking for some of the tools for embedding a model in your website, TensorFlow JS is one of our favorites.
Stage 5. Revise and iterate
As you get more experience in the field, make sure to go back to your very first project at some point. Review it, and see where some of the things you thought made sense at the time look completely out of place now. Figure out why this happened. Once you are done self-reflecting – do that project all over again. Same data, but new approaches and practices that you’ve learned over time.
Trust us, this is one hell of a measuring stick. In retrospect, it will put you out of your comfort zone, as it should. But it will definitely make you feel better and perform better going forward.
These tips will help get on track once you decide to begin your very first data science project. A final word of advice – it may seem complex, but so are many other things. On its own, data science is less of a solution and more of an important tool. It’s not a magic stick, so don’t expect it to just work out on its own. Goals, metrics, and self-discipline will ensure that you actually follow through and get the project done.
Tip: If you have a machine learning project idea and need guidance or hands-on expertise, feel free to contact us and we’ll be happy to help out!