Becoming a no code machine learning expert in 2022: Benefits and use cases

How can you become a machine learning expert without any coding experience?

In 2022, machine learning has become so accessible that anyone with basic business skills is able to use it. Machine learning is already being used by businesses in every industry to improve their operations. 

In this article, we will explore the benefits of becoming a no code machine learning expert and some of the use cases that you can implement.

What is machine learning? 

In the business world, there’s a lot of talk about artificial intelligence and machine learning. But what’s the difference between the two?

AI is the umbrella term that refers to any system that can perform tasks that ordinarily require human intelligence, such as understanding natural language and recognizing objects in pictures. ML is a subset of AI that relies on data and algorithms to learn how to do tasks like identify spam email or predict consumer behavior.

Traditionally, implementing AI or ML required a high level of technical expertise. You needed to know how to write complex code to get your system up and running. But that’s changing. Thanks to the growing popularity of no-code platforms, anyone can now use AI and ML. These platforms allow you to create sophisticated applications using machine learning without code.

No-code platforms work by allowing you to drag and drop components called widgets into a visual interface. Widgets are like Lego blocks that you can use to build your application. They come with pre-configured settings that allow you to get started immediately.

To create a machine learning model, you simply connect a dataset and select a column, and the platform will take care of the rest. It will automatically select the best algorithms and configure them for you. This democratization of AI and ML is a game changer. It means that all teams, regardless of their technical expertise, can now use these technologies to improve their business.

For example, let’s say you’re a small business owner who wants to use AI to improve your customer service. With a no-code platform, you can easily create a system that can understand natural language and respond to customer queries.

Or let’s say you’re a marketer who wants to use machine learning to improve your website’s conversion rate. With a no-code platform, you can easily create a system that can analyze your website traffic and identify the factors that are most correlated with conversion.

Traditional Machine Learning vs No-Code Machine Learning 

Since the 1950s, researchers have been working on artificial intelligence and machine learning, with the aim of developing machines that can learn and work like humans. It wasn't until the 1990s, with IBM's Deep Blue defeat of world chess champion Garry Kasparov, that machine learning really gained traction.

More recently, the explosion of big data, supercomputers, and sophisticated algorithms has taken machine learning to new heights. Still, there are two ways to develop machine learning models: the traditional approach of writing code and the newer no-code approach.

Code-based machine learning requires a lot of expertise and can be very time-consuming. Programming languages like Python and R are commonly used in conjunction with libraries like TensorFlow, Keras, and PyTorch to develop custom models, but that's not all. Data scientists also need to have a deep understanding of statistics and linear algebra.

Not to mention, model-building isn't the only thing that takes time. Data preparation, which can involve everything from data cleaning to feature engineering, is another critical but time-consuming step. Further, building data pipelines and integrations with other systems can be complex and time-consuming as well.

The no-code machine learning approach, on the other hand, uses point-and-click interfaces to build models without any code. This means that even people with no coding experience can build sophisticated machine learning models in far less time than it would take using the traditional approach.

There are a number of platforms that offer no-code machine learning, including Akkio, Google's AutoML, Amazon's Sagemaker, and Microsoft's Azure ML. We offer a comparison of these no-code machine learning platforms.

Benefits of machine learning without code

No-code AI platforms are enabling businesses to make better decisions by automating the process of data collection, analysis, and action. Traditionally, businesses have had to hire data scientists and developers to build custom AI applications. However, with no-code AI, these tasks can be completed without any data science or programming experience. This has made AI more accessible to businesses of all sizes and allowed them to quickly and easily incorporate AI into their operations.

The benefits of no-code AI are manifold:

1. Quick and automated machine learning processes
2. Easy incorporation of AI into business operations
3. Better decision-making
4. Improved resource optimization

Businesses are seeing results across the board thanks to no-code AI. Let's take a look at these benefits in greater detail.

1. Quick and automated machine learning processes

No-code AI platforms automate the end to end machine learning process, businesses are able to quickly identify trends and patterns that would otherwise be difficult to detect. This allows businesses to make better decisions based on accurate and timely data.

Additionally, no-code AI platforms automate the analysis process, meaning businesses can quickly identify the root cause of problems and find solutions. Further, no-code AI platforms enable businesses to take action quickly and efficiently by integrating real-time predictions in any environment. This allows businesses to respond to problems and opportunities as they occur, instead of after the fact.

2. Easy incorporation of AI into business operations

Instead of developing custom integrations, businesses can select pre-built integrations that are available on most no-code AI platforms. This means businesses can get started with AI quickly and without any technical expertise.

For instance, sales and marketing teams can integrate with Hubspot and Salesforce in clicks, without writing any code. This allows them to start using AI to automate tasks, such as lead scoring and contact segmentation.

3. Better decision-making

No-code AI platforms enable businesses to make better decisions by automating the process of data collection, analysis, and action. Traditionally, businesses have had to hire data scientists and developers to build custom AI applications.

However, with no-code AI, these tasks can be completed without any data science or programming experience. This has made AI more accessible to businesses of all sizes and allowed them to quickly and easily incorporate AI into their operations.

4. Improved resource optimization

No-code AI can also help businesses optimize their resources. For example, many businesses use machine learning to improve their customer experience. ML can be used to determine which products a customer is likely to buy and then recommend those products to the customer. This type of optimization can be difficult to do without no-code AI platforms. By automating the process, no-code AI makes it easy for businesses to take advantage of ML’s benefits.

Leading use-cases of machine learning without code

The business use-cases of no code machine learning are manifold, and can vastly improve a company’s bottom line. In marketing, no code machine learning is used for tasks like augmented lead scoring, text classification, churn reduction, predicting customer LTV, and more.

1. Lead scoring

First, let’s look at how machine learning is used in lead scoring. In traditional lead scoring, a marketer assigns values to different leads based on their likelihood of converting into customers. This process is often done through a combination of biases and gut feeling, and it can be time-consuming and inaccurate. However, with the help of machine learning, lead scoring can become far more accurate.

Machine learning can be used to automatically score leads based on data such as their demographics, interests, and past behavior. This allows businesses to quickly and accurately identify the most promising leads, which can then be sent to sales representatives for follow-up. As a result, businesses can see a significant increase in their conversion rates.

2. Text classification

Text sentiment classification is another task that can be greatly improved with the help of machine learning. Text sentiment classification is the process of identifying the attitude of a customer towards a product or service. This information can be used by businesses to make better decisions about what products to offer and how to market them.

Machine learning can be used to automatically identify the sentiment of customer reviews and feedback. This information can then be used to improve the customer experience by identifying and addressing negative sentiment early on. As a result, businesses can see a significant reduction in customer churn.

3. Predicting customer LTV

Predicting customer LTV is another important task that can be greatly improved with machine learning. LTV is the amount of revenue a customer is expected to generate over the course of their relationship with a business. Predicting LTV is important for businesses because it allows them to make informed decisions about how much to invest in acquiring new customers and retaining old ones.

Machine learning can be used to predict LTV by analyzing data about past customers. This data can include things like purchase history, engagement levels, and social media data. By using this data, businesses can get a better understanding of which customers are most likely to be profitable. As a result, businesses can allocate their marketing resources more effectively, resulting in a higher return on investment.

4. Optimization

More broadly speaking, let’s look at how machine learning is used in optimization. In business, optimization is the process of making decisions that improve the performance of a company. This can be done in a variety of ways, such as improving the efficiency of operations or increasing the conversion rate of sales leads.

Machine learning can be used in optimization by identifying patterns in data. This data can be used to improve decision-making by providing insights into how different decisions impact the performance of a company. As a result, businesses can see a significant improvement in their bottom line.

5. Predicting the future

Beyond gaining historical insights and optimizing for the present, machine learning is also used to forecast future events. This is done by using past data to train a machine learning algorithm to make predictions about future events.

Forecasting is important for businesses because it allows them to plan for the future. By knowing what is likely to happen, businesses can make informed decisions about things like inventory, staffing, and investment.

Machine learning can be used to forecast a wide range of events, including things like future sales, customer churn, and even election outcomes. By using machine learning, businesses can get a better understanding of the future and make decisions that will help them thrive in whatever comes their way.

6. Fraud detection

Finally, let’s look at how machine learning is used for fraud detection. As businesses become more and more reliant on digital technologies, the risk of cybercrime increases. Fraud is a type of cybercrime that involves illegally obtaining money or other assets from a business.

Machine learning can be used for fraud detection by identifying patterns in data that are indicative of fraud. This data can include things like customer data, purchase data, and financial data. By using machine learning, businesses can get a better understanding of how fraud is perpetrated and take proactive measures to prevent it.

As you can see, the business use-cases of machine learning are vast and varied. By using machine learning, businesses can improve their bottom line in a wide range of ways. 

In marketing, machine learning can be used for tasks like lead scoring, text sentiment classification, churn reduction, and predicting customer LTV. In optimization, machine learning can be used to identify patterns in data and make better decisions. And in forecasting, machine learning can be used to predict future events. Finally, machine learning can also be used for fraud detection.

No-code machine learning is even making its way to computer vision, such as object detection and image classification, with AI tools like Lobe.ai, Teachable Machine, and RunwayML. These no-code machine learning tools similarly use techniques like deep learning, but for images and videos instead of data tables.

How can you apply no code machine learning to your business?

Machine learning can seem overwhelming to the uninitiated. There are so many different languages, algorithms, and tools to choose from. But with the rise of no-code machine learning platforms, you don't need to be a data scientist to build and deploy models.

Many so-called "automated machine learning" or "no-code machine learning" platforms are actually low code platforms, which require some technical expertise. True no-code platforms don't require any skills in tools like Python or MakeML, or in fields like neural networks or software development.

Akkio is a true no-code machine learning platform. With our drag-and-drop interface, it's easy to build and deploy models without any coding required. Perhaps even more importantly, it's 100X faster and far cheaper than legacy AutoML platforms.

Suppose you run an e-commerce business and want to predict whether a customer will abandon their cart. The traditional method for doing this would be to build scalable data pipelines, ingest data into a data warehouse, and then use machine learning algorithms to build a model. This process can be time consuming and expensive.

With Akkio, you can quickly build a predictive model without any coding required. All you need to do is connect a relevant data source, select the column you want to predict, and Akkio takes care of the rest. The dataset can be a CSV or a connection to any tools typical to a business analyst’s workflow.

Akkio will automatically identify the best machine learning algorithm for the task and train models. After model training, which takes a matter of seconds, you can deploy ML models and start making predictions in production. You can also view the model performance, including error metrics.

With Zapier's no-code automation platform, you can connect Akkio to thousands of other apps to get your work done. You can also directly connect Akkio to business applications like Snowflake, Salesforce, Hubspot, and more to get insights and predictions about how your business is performing. More technical teams can integrate their models anywhere with the Akkio API.

Akkio is the perfect tool for busy professionals who want to take advantage of machine learning without needing to learn coding or statistics. Any startup can see the importance of a fast time-to-market, and Akkio enables businesses to bring predictive analytics models to production far faster than legacy tools, and with more pragmatic pricing. From segmentation to natural language processing, Akkio lets teams build AI models without needing to hire programmers.

Conclusion

There is no denying that artificial intelligence is a hot topic these days. And if you want to get in on the action, but don't have any coding expertise, don't worry – there are options out there for you.

One such option is Akkio, a tool that allows you to build AI models without any coding required. In fact, you don't even need to have any technical expertise at all. Akkio makes it easy for anyone to get started with AI, regardless of their experience level.

So if you're looking to get into AI but don't know where to start, an Akkio free trial is a great place to start. With its easy-to-use interface and wide range of features, Akkio makes it easy for anyone to build AI models and harness the power of machine learning.