Computer scientists using Data sciences and machine learning will build statistics, probability, and computation into successful business models.

Developers plus Data Science Data Analysis and Data Base Engineering.

Developers using Data science and machine learning will rely on their knowledge of statistics, probability, and computation to successfully build models. 

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July 25, 2022

Massimo Re

Abstract: This article explains machine learning practices and how it is inevitable for developers to study rigorously with data scientists. The report clearly shows the lousy human habit of confining knowledge to nonexistent boundaries, while the possible synergies are endless.

1. Index
2. Machine learning (ML)
3. Supervised and unsupervised Machine Learning Algorithms
4. Supervised Machine Learning 
5. Unsupervised Machine Learning 
6. Choosing a machine learning approach best suits your needs.
7. The business goal of machine learning is to model the customer's functional life value.
8. Model customer churn rate using machine learning.
9. Model customer churn rate using machine learning.
10. The business goal of machine learning is to reach target customers by segmenting customer needs.
11. Product recommendation engines are considered attractive based on big data and machine learning.
12. Forecasting potential of Machine Learning. A feature of machine learning is its predictive ability.
13. The enormous potential of machine learning can help companies transform the amount of data currently available into business value.

Machine learning (ML) 

Machine learning (ML) is a subset of Artificial Intelligence (AI) concerned with creating systems that learn or improve performance based on the Data they use. Artificial intelligence is a generic term and refers to systems or machines that mimic human intelligence. 

We often use together and interchangeably Machine learning and AI, but they don't mean the same thing. An important distinction is that while everything related to machine learning falls under artificial intelligence, artificial intelligence doesn't just include machine learning. 

We use Machine learning everywhere today. When we interact with banks, shop online, or use social media, we use machine learning algorithms to make our experience efficient, easy, and safe. Machine learning and associated technology are developing rapidly, and we are just starting to discover their capabilities.

Supervised and unsupervised Machine Learning Algorithms

Two Approaches to Learning Algorithms are the engines that power machine learning. Supervised machine learning and unsupervised learning are the principal machine learning algorithms currently used. The difference between these two types is defined by how each algorithm learns the data to make predictions.

Supervised Machine Learning 

Supervised machine learning algorithms are the most widely used. With this model, a data scientist acts as a guide and teaches the algorithm the results to generate. 

Just as a child learns to identify fruits by storing them in a picture book, in supervised machine learning, the algorithm learns from an already labeled data set with a predefined output. 

Linear and logistic regression algorithms, multiclass classification, and support vector machines are some examples of supervised machine learning.

Unsupervised Machine Learning 

Unsupervised machine learning uses a more independent approach, in which a computer learns to identify complex processes and patterns without a person's careful and constant guidance. Unsupervised machine learning involves training based on data without labels. No specific output is defined. 

To continue using the previous analogy, using unsupervised machine learning is akin to a child learning to identify fruits by looking at the colors and patterns rather than memorizing the names with the help of a teacher. 

The child will look for similarities between the images and divide them into groups, assigning each group the new corresponding label. K-means clustering algorithms, principal and independent component analysis, and association rules are examples of unsupervised machine learning.

Choosing a machine learning approach best suits your needs.

A supervised or unsupervised machine learning algorithm typically depends on factors related to the structure and volume of your data and the use case you want to apply.

Machine learning has been adopted across a range of industries to support a variety of business objectives and use cases, including Customer Lifecycle Calculation, Anomaly Detection, Value Dynamic Pricing, Predictive Maintenance, Classification of images, and Recommendation Engines.

Choosing a machine learning approach best suits your needs. 

When developers start using machine learning, they will rely on their knowledge of statistics, probability, and computation to successfully build models that learn over time. 

With strategic skills in these areas, developers should have no problem learning the tools many other developers use to train modern ML algorithms. 

Developers can also decide whether they will check the algorithm or not. 

A developer can make decisions and set up a model in advance in a project and then allow the model to learn without further involvement of the developers. 

There is often a fluid dividing line between developers and data scientists. Sometimes developers will synthesize data using a machine learning model, while data scientists help develop solutions for end users. Collaboration between these two disciplines can make ML projects more valuable and useful.

The business goal of machine learning is to model the customer's functional life value.

Creating a model for calculating customer lifecycle value is critical for e-commerce businesses and applicable in many other industries.

Organizations use machine learning algorithms in this model to identify, understand and retain their most important customers. 

These models evaluate vast amounts of customer data to determine which shoppers are the most loyal or a combination of these two types of customers.

Models for calculating customer lifecycle value are particularly effective in predicting the future revenue a company will earn from a single customer over a given period.

This information allows companies to focus on marketing strategies to encourage high-value customers to interact more with their brands.

Models like this also help organizations target their acquisition spending to attract new customers with similar profiles to existing high-value customers.

Model customer churn rate using machine learning.

Acquiring new customers takes more time and money than keeping existing customers satisfied and loyal.

Creating a model for calculating customer churn helps companies identify customers likely to stop interacting with them and the reasons for churn.

A practical model uses machine learning algorithms to provide comprehensive insights - from individual customer churn risk scores to top churn factors, ranked in order of importance.

These findings are critical to the development of an algorithm-based retention strategy.

Gaining a deeper insight into customer churn helps businesses optimize discount offers, email campaigns, and other targeted marketing initiatives that keep high-value customers from shopping and returning for more purchases.

Consumers have more choices than ever before and can instantly compare prices across various channels.

Dynamic pricing, also known as demand-based pricing, allows companies to stay on top of rapidly changing market dynamics.

It allows organizations to flexibly price items based on factors such as the target customer's interest level, demand at the time of purchase, and whether the customer was interested in a marketing campaign.

This level of business agility requires a reliable machine learning strategy and a wealth of data to understand how customers' willingness to pay for a good or service changes in different situations. Despite the complexity of dynamic pricing models, companies such as airlines and shared transportation services have successfully implemented active pricing optimization strategies to maximize revenue.

Model customer churn rate using machine learning.

Acquiring new customers takes more time and money than keeping existing customers satisfied and loyal.

Creating a model for calculating customer churn helps companies identify customers likely to stop interacting with them and the reasons for churn.

A practical model uses machine learning algorithms to provide comprehensive insights - from individual customer churn risk scores to top churn factors, ranked in order of importance.

These findings are critical to the development of an algorithm-based retention strategy.

Gaining a deeper insight into customer churn helps businesses optimize discount offers, email campaigns, and other targeted marketing initiatives that keep high-value customers from shopping and returning for more purchases.

Consumers have more choices than ever before and can instantly compare prices across various channels.

Dynamic pricing, also known as demand-based pricing, allows companies to stay on top of rapidly changing market dynamics.

It allows organizations to flexibly price items based on factors such as the target customer's interest level, demand at the time of purchase, and whether the customer was interested in a marketing campaign.

This level of business agility requires a reliable machine learning strategy and a wealth of data to understand how customers' willingness to pay for a good or service changes in different situations. Despite the complexity of dynamic pricing models, companies such as airlines and shared transportation services have successfully implemented active pricing optimization strategies to maximize revenue.

The business goal of machine learning is to reach target customers by segmenting customer needs.

 The most effective marketing strategy has always been to offer the right product to the right person at the right time.

Not long ago, marketers relied on intuition to segment customers, breaking them into groups for targeted campaigns.

Today, machine learning enables data scientists to use clustering and classification algorithms to divide customers into groups based on specific characteristics.

These groups consider customer differences based on multiple dimensions, such as demographics, browsing behavior, and affinity.

Linking these characteristics to purchasing behavior patterns allows data-savvy companies to launch personalized marketing campaigns that are more effective than generic sales incentive campaigns.

As the data available to businesses increases and algorithms reach a higher sophistication level, so will personalization capabilities, allowing companies to move closer to their ideal customer segment.

Harness the power of image classification.

Machine learning supports a wide range of use cases that go beyond retail, financial services, and e-commerce.

It also has enormous application potential in the science, health, construction, and energy sectors.

Image classification uses machine learning algorithms to label a predefined group of categories or any input image. 

It enables companies to create 3D construction plan templates based on 2D designs, facilitate photo tagging in social media, communicate clinical diagnoses, and much more.

Deep learning methods such as neural networks are often used for image classification because they can more effectively identify the most relevant features in the presence of potential complications.

They can consider variations in the view, lighting, scale, or volume of unnecessary image information and compensate for these to provide the most relevant and highest quality insights.

Product recommendation engines are considered attractive based on big data and machine learning.

Recommendation engines are essential for addressing consumer cross-selling and up-selling strategies and delivering a better customer experience.

The most common example is Amazon's recommendation bar, which by suggesting what customers have bought who looked at a particular item, increases sales by no less than 20% per year.

Recommendation engines use machine learning algorithms to examine large amounts of data and determine the likelihood that a customer will purchase an item or watch the content, then provide personalized recommendations to the user.

The result is a more personalized and relevant experience that improves engagement. It reduces abandonment and, in any case, increases the stay on the site with undeniable benefits regarding SEO search engine optimizations.

Forecasting potential of Machine Learning. A feature of machine learning is its predictive ability.

Managers often make their business decisions are usually made based on historical results.

Using machine learning technology uses advanced data analytics to make predictions.

Organizations can make proactive decisions and match them with past data.

Predictive maintenance can enable manufacturers, energy companies, and other industries to take the lead and ensure their operations remain reliable and optimized.

Honestly, there are already predictive models based on traditional statistics that allow excellent forecasting capabilities used by oil companies. 

Still, in an oil field with hundreds of drills in operation, machine learning models can identify equipment at risk of malfunctioning in the short term. 

Term and then notify the maintenance teams in advance. This approach optimizes productivity and increases asset performance, uptime, and equipment life.

It can also minimize worker risks, reduce liabilities and improve regulatory compliance.

The benefits of predictive maintenance extend to inventory control and management.

Eliminating unplanned equipment downtime by implementing predictive maintenance helps organizations predict the need for parts and repairs more accurately, significantly reducing capital and operational costs.

The enormous potential of machine learning can help companies transform the amount of data currently available into business value.

However, inefficiency in handling inefficient data flows can prevent companies from fully exploiting this potential.

Machine learning is an important KPI that must include a comprehensive platform that helps organizations simplify operations and implement large-scale models.

The right solution will enable companies to centralize all data science activities in a collaborative platform and accelerate the use and management of open source tools, frameworks, and infrastructures.