Machine learning (ML) is the branch of AI in which a program learns patterns from data instead of being explicitly coded with rules. Deep learning (DL) is a subset of machine learning that uses multi-layer neural networks to learn those patterns directly from raw data such as images, audio and text. All deep learning is machine learning; most machine learning in business is not deep learning.
What machine learning is
Classic machine learning works on structured data: rows and columns of customer attributes, transactions, sensor readings. A human chooses the features ("days since last purchase", "average order value"), an algorithm such as logistic regression, random forests or gradient boosting learns how those features relate to an outcome, and the model predicts that outcome for new rows.
Typical uses: credit scoring, churn prediction, demand forecasting, fraud detection, price optimisation, recommendation on tabular signals. These remain the highest-ROI AI projects in most companies, and most of them are won by well-engineered boosting models, not by neural networks.
What deep learning is
Deep learning replaces hand-made features with layers of artificial neurons that learn their own representations. Feed a network millions of images and it discovers edges, then shapes, then faces, without anyone defining "edge". The same idea, applied to text, produced transformers and the large language models behind ChatGPT, Claude and Gemini.
Typical uses: image recognition and medical imaging, speech-to-text, machine translation, generative AI, self-driving perception, protein folding. Deep learning needs more data, more compute (GPUs) and more engineering care, which is why it dominates perception and generation problems and rarely beats boosting on small tabular datasets.
- Input layerRaw pixels, audio samples or tokens enter the network.
- Hidden layersEach layer transforms the previous one; depth lets the network build abstract concepts.
- Loss and backpropagationThe network's error is measured and pushed backwards to adjust millions of weights.
- ArchitecturesCNNs for images, RNNs for sequences, Transformers for language and increasingly everything.
Side-by-side comparison
Jobs and salaries: ML engineer vs deep learning engineer
Hiring is broad for machine learning (every analytics team) and narrower but better paid for deep learning (vision, speech, LLM teams, research labs). The sweet spot in 2026 is an engineer who can do classic ML well and work with pretrained deep models through fine-tuning, embeddings and LLM APIs.
How to learn ML and then DL
Which Aegis path fits you?
Learn ML and deep learning at Aegis
Both are taught hands-on inside these Aegis programs, with projects on real datasets and deployment to cloud:
Full-Time PGP in Data Science, Business Analytics & AI
11 months · On-campus Mumbai · Since 2015
India's first PGP in Data Science, launched with IBM. Statistics to deployment, with industry projects and 500+ hiring partners.
Aegis programPGP in GenAI & AI Agents
11 months · Hybrid · 11 portfolio projects
LLMs, RAG, multi-agent orchestration, MCP, MLOps and responsible AI, with a 12-week capstone.
Frequently asked questions
Is deep learning better than machine learning?
Neither is better; they solve different problems. On tabular business data, gradient-boosted machine learning usually wins. On images, speech and language, deep learning wins decisively.
Do I need to learn machine learning before deep learning?
Yes, in practice. Model validation, overfitting, metrics and feature thinking transfer directly, and most deep-learning jobs still involve classic ML work.
Which pays more, ML engineer or deep learning engineer?
Deep-learning and LLM specialists earn a premium (indicatively ₹7–16 LPA entry rising to ₹32–60 LPA senior in India, 2026) over general ML engineers, but there are far more ML engineering openings.
Is ChatGPT machine learning or deep learning?
Both: it is a deep-learning model (a transformer neural network), which is a type of machine learning. The product around it also uses classic ML for ranking, safety and routing.
How much maths is needed for deep learning?
Working knowledge of linear algebra (matrices), calculus (gradients) and probability. Frameworks like PyTorch do the computation; you need enough maths to debug and reason, not to prove theorems.