Do you know what Machine Learning is? In free translation from English, the term carries the meaning of “machine learning”, which can understand as a data analysis method that automates the construction of analytical models.
From this application, it will understand that the concept will directly link to a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention, as an example of what some digital platforms, internet of things systems and other innovations can already do.
In general, it can say that Machine Learning is a technology where computers can learn according to the expected answers through associations of different data, which can be images, numbers and everything that this technology can decode.
Table of Contents
Machine Learning vs Traditional Software
| Aspect | Traditional Software | Machine Learning |
|---|---|---|
| Logic | Rule-based | Data-driven |
| Adaptability | Static | Learns over time |
| Maintenance | Low | Continuous |
| Accuracy | Deterministic | Probabilistic |
| Best Use | Stable rules | Complex, changing patterns |
In all three streaming services, machine learning will primarily use for personalization. The technology is constantly improving recommendation algorithms and shaping the catalogue of movies, series, podcasts and music according to user responses.
Yes, each of these services offers, in a unique and personalized way, options that suit you from data collected based on your preferences and consumption patterns within the platforms themselves.
How Machine Learning Works in Practice
Data Collection: The Messy Reality
Real-world datasets are incomplete, noisy, and biased. Missing values, duplicated records, and inconsistent labeling are common challenges.
Real ML Workflow vs Time Allocation
| ML Stage | Average Time Spent |
|---|---|
| Data collection & cleaning | 60–80% |
| Feature engineering | 10–15% |
| Model training | 5–10% |
| Deployment & monitoring | 5–15% |
Feature Engineering: The Silent Performance Multiplier
Well-designed features often outperform complex algorithms trained on raw data. In practice, a simple model with strong features beats a complex model with weak inputs.
Deployment & Model Drift
Once deployed, models begin degrading as real-world behavior changes — a phenomenon known as model drift. Without monitoring, accuracy drops silently.
Common Machine Learning Challenges Nobody Talks About
| Challenge | Why It Matters |
|---|---|
| Data leakage | Inflated performance metrics |
| Bias | Unfair or illegal outcomes |
| Model drift | Silent accuracy loss |
| High maintenance cost | Underestimated budgets |
| Explainability | Regulatory requirements |
Types of Machine Learning With Real Examples
Supervised Learning
Used for prediction tasks:
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Credit scoring
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Medical diagnosis
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Demand forecasting
Unsupervised Learning
Used for pattern discovery:
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Customer segmentation
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Fraud detection
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Market analysis
Reinforcement Learning
Used where decisions impact future outcomes:
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Robotics
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Dynamic pricing
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Autonomous systems
Machine Learning Statistics & Trends (2026)
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70%+ enterprises use ML in at least one core process
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60–80% of ML effort goes into data preparation
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ML-driven automation improves targeted workflows by 20–30%
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Demand for ML-literate professionals continues to rise
Google Adwords, Facebook Ads And Instagram Ads: Well-Targeted Ads With Machine Learning
Ads on search engines and social networks work based on auctions, as they will sponsor. In this way, they are advertisements that place those who pay the most in the first “appearance” positions. That is, the winner will have their ad displayed to the user.
Auctions run incessantly daily; it streamlines the process with intelligent bidding.
The strategy makes campaigns more profitable through predictive click-through rates (CTR) and conversion estimates based on user behaviour.
With machine learning, companies of all sizes see increased return on investment (ROI) on ad platforms.
Google Translate: Machines That Learn Other Languages
Created in 2006, Google Translate has the proposal to transcribe and translate sentences instantly in more than 100 languages.
Do you remember what the first translations were like? They have become increasingly accurate thanks to it
The technology has allowed the tool to learn according to user research. Today, Google Translate can translate texts into images through the smartphone camera, showing the evolution of this learning from using a system with data collection.
Lu From Magalu And The Example Of Machine Learning In Virtual Assistants
If you shop online, you must have come across Lu. Magazine Luiza’s virtual assistant interacts more and more naturally with users with the help of it. Lu da Magalu, by the way, is already considered a successful case of branded content for having increased engagement between the brand and the public.
Machine learning allowed Magazine Luiza to constantly offer customer service and have a system directly integrated with the company’s data without needing a human interface for consultation.
The brand ended 2020 with the highest revenue in its history: BRL 43.5 billion. Today Magazine Luiza is the Brazilian leader in multichannel retail and traditional e-commerce.
Market Trends With Machine Learning
Significant changes are underway in the world of marketing, technology and large corporations looking for innovation and significant advancements in the market. These changes are primarily related to the power of machine learning.
Its impact is so significant that97% of leaders believe the future of marketing will consist of experienced professionals working in collaboration with machine learning-based automation entities.
Machine learning techniques will use to solve several diverse problems. As a result, companies can benefit their business as we move into a world of data, channels, content and contexts of extreme convergences.
For the current marketing team, it is about finding pieces of predictive knowledge in structured and unstructured data and using it to your advantage.
The ability to respond quickly and accurately to changes in customer behaviour is the gamble of today’s world.
Need To Keep Up To Date And Keep Up With Market Developments
The area demands constant training because it is already present and will practically connect to users at all times. It is necessary to seek updating and recognition of It processes from courses and training.
One of the most comprehensive options that serve this purpose today is the ESR Introduction to Data Science course.
It offers introductory content that explores the historical evolution of these engines, Analytics & Big Data, as well as ethical issues about Data Science, LGPD, machine learning and much more.
Another relevant research material is the Data Science Webinar conducted by ESR. The material intends to allow a broad learning experience in the different roles that make up an Analytics team, introducing the main guidelines surrounding Data Science and motivating the viewer to delve into the thematic.
The Future of Machine Learning Beyond 2026
The future is not just larger models, but:
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Better data governance
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Responsible AI systems
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Human-in-the-loop workflows
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Continuous evaluation and transparency
Final Takeaways
Machine Learning in 2026 is about execution, not experimentation.
Organizations that succeed:
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Invest in data foundations
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Monitor models continuously
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Align ML with business outcomes
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Avoid hype-driven decisions
Those who treat ML as a living system, not a one-time project, will outperform competitors.
