Let's come to the point... (Introduction later 🤘)
What makes things different? If we learn what is different in all terms that come under one big umbrella, which is AI, then it will be easy for us to learn more in these fields.
Let's do it.
Listen, guys, I am not going to discuss the definition. This is totally raw stuff, okay?!
1. AI [Artificial Intelligence]
The broadest umbrella that covers everything. It's about creating machines that can mimic human intelligence.
But remember, it is not just about mimicking human intelligence but also making decisions autonomously and learning from interaction.
Includes:
- Machine Learning (ML)
- Deep Learning (DL)
- Natural Language Processing (NLP)
- Computer Vision (CV)
- Robotics & Automation
2. ML [Machine Learning]
ML is the subset of AI, one machine learns from past data to predict future outcomes without being explicitly programmed for specific task.
Example: Predicting house prices based on features like size and location.
Includes:
- Supervised Learning: Predict house prices
- Unsupervised Learning: Customer clustering
- Reinforcement Learning: Training an AI agent for games
Job Roles:
- ML Engineer: Builds models, optimizes algorithms
- ML Developer: Focuses on integrating ML models into products
3. DL [Deep Learning]
A subset of ML, the Same things learn from the past data and try to predict the future, but DL has more power like [ML + Some energy booster] 😅.
Using a Neural Network [Layer of the network where each layer thinks - so more layers it thinks more], Machine solves complex problems like generative AI, and Natural Language Processing [NLP - talk and understand human language].
One big difference is in ML we choose features like size, color, etc from input, there are like parameters that we use to train the ML Model that is called feature engineering,
But in DL this is done automatically means feature selection does automatically that's why it can be called a*utomatic feature extraction (instead of "feature engineering").*
Includes:
- ANNs: General neural networks
- CNNs: Image recognition
- RNNs: Time-series forecasting
Job Roles:
- AI Developer (DL Focus): Develops complex AI systems
- Computer Vision Engineer: Focus on image-related models
- NLP Engineer: Language models (chatbots, translation)
4. DS [Data Science]
Focuses on data analysis, pattern discovery, and storytelling using data.
Mainly focus on the data part, which includes data analysis, finding insights, and storytelling using data and facts, in short, find something helpful in data with patterns to take any decision that helps.
Key outcome: Data-driven decision-making.
Includes:
- Data Cleaning & Preprocessing
- Visualization & Reporting
- Predictive Analytics
Job Roles:
- Data Scientist: Builds insights and predictive models
- Data Analyst: Analyzes and visualizes data
5. Robotics
Building physical machines with sensors, AI, and automation logic.
Robotics doesn't always need AI. Basic robots can be pre-programmed without AI. But when AI is integrated, they become intelligent and adaptable.
Example: Boston Dynamics' robots vs simple pick-and-place industrial robots.
Includes:
- Self-driving cars
- Robot assistants (e.g., industrial robots)
Job Roles:
- Robotics Engineer: Develops hardware + software
- AI Robotics Specialist: Integrates AI for intelligent behavior
6. Automation
Using software/hardware to perform tasks without human intervention.
Software Automation:
Automates virtual tasks (RPA for data entry).Hardware Automation:
Automates physical tasks (like robotic arms in factories or smart home systems).
Includes:
- RPA (Robotic Process Automation): Automating repetitive business tasks
- AI Automation: Smart decision-making systems
Job Roles:
- Automation Engineer: Focus on automation solutions
7. Data Engineering & ML Ops
Ensures data pipelines and model deployments are smooth.
Data Engineering: Building pipelines to process and move data efficiently for analytics or AI models.
ML Ops: Deploying, scaling, and maintaining ML models in production environments.
Includes:
- Cloud infrastructure (AWS, GCP)
- CI/CD for ML models
Job Roles:
- ML Ops Engineer: Focuses on deploying AI/ML systems
- Data Engineer: Builds pipelines for large-scale data
Summary Table 🧠
| Field | Focus Area | Job Roles |
|---|---|---|
| AI | Broad decision-making | AI Developer, AI Engineer |
| ML | Learning from data | ML Engineer, ML Developer |
| DL | Neural networks | DL Specialist, NLP Engineer |
| Data Science | Analytics & predictions | Data Scientist, Analyst |
| Robotics | Smart machines | Robotics Engineer |
| Automation | Task automation | Automation Engineer |
| Data Engineering | Data pipelines | Data Engineer, ML Ops |
Okay, a little bit of time for my introduction, My name is Jaimin Bariya, and if you find this article helpful plz give all likes (all 5), and throw one comment (anything like nice, super, greetings, etc... ), if it is possible then share with your one friend.
Okay Bye Bye, See you in the next article and try to simplify more hard things 😅.
Checkout my previous articles, if you like AI, you will like them
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