Artificial Intelligence (AI) might seem like magic—but behind the scenes, it’s powered by math. If you’re just beginning your journey into AI and wondering where to start, the answer is simple:
Start with the math.
Why? Because every AI model—from the simplest regression to the most advanced neural network—is built on mathematical concepts.
In this blog, we’ll break down what to learn first, why it matters, and how to learn it effectively. This post kicks off a series that will guide you step-by-step through everything you need to become confident in AI.
1. Linear Algebra — The Language of AI Models
Artificial Intelligence(AI) models deal with large amounts of data, and that data is stored in vectors and matrices. Linear algebra gives us the tools to process, transform, and represent this data efficiently.
Key Topics:
- Scalars, Vectors, Matrices, and Tensors
- Matrix Multiplication
- Dot Products & Transpose
- Identity and Inverse Matrices
- Eigenvalues and Eigenvectors
Real Use Case:
Neural networks use matrix multiplication to compute outputs and pass data between layers.
Learn From:
- Essence of Linear Algebra by 3Blue1Brown (YouTube)
- Gilbert Strang’s Linear Algebra (MIT OCW)
2. Calculus — For Learning and Optimization
Calculus helps machines learn. How? Through optimization. When a model makes a prediction, we calculate how wrong it is (loss function) and adjust the model to improve. That adjustment uses derivatives and gradients, core calculus concepts.
Key Topics:
- Derivatives and Partial Derivatives
- Chain Rule
- Gradient Descent
- Maxima and Minima (Optimization)
- Integrals (Basics only)
Real Use Case:
Backpropagation in deep learning uses calculus to adjust weights during training.
Learn From:
- Khan Academy: Calculus Basics for Calculus is critical for Artificial Intelligence(AI)
- Calculus for Machine Learning by Jason Brownlee
3. Probability and Statistics — The Core of Uncertainty
AI isn’t about certainty—it’s about probabilities. From spam detection to predicting stock prices, AI uses probability to make sense of uncertain data.
Key Topics:
- Basic Probability & Conditional Probability
- Bayes’ Theorem
- Random Variables & Distributions (Normal, Binomial, Poisson)
- Mean, Median, Variance, Standard Deviation
- Hypothesis Testing & Confidence Intervals
Real Use Case:
Naive Bayes classifier predicts the probability of an email being spam based on word frequency.
Learn From:
- Think Stats by Allen B. Downey (Free PDF)
- StatQuest (YouTube): Clear and entertaining explanations
4. Optimization — How Machines Learn from Mistake
Optimization is about making AI models better. Every time you train a model, it’s trying to minimize a loss function using optimization techniques like gradient descent.
Key Topics:
- Cost/Loss Functions
- Convex Functions
- Gradient Descent (Batch, Stochastic)
- Learning Rate, Momentum, Adam Optimizer
Real Use Case:
All AI models—from linear regression to GPT—use optimization algorithms to reduce error.
Learn From:
- DeepLizard’s Optimization Series (YouTube)
- ml-playground.com (interactive visualizations)
What’s Next?
In the next blog post, we’ll break down Machine Learning 101—where you’ll finally start building models. But trust me, don’t skip the math. Without it, AI feels like guessing. With it, AI becomes empowering.
Final Thoughts
If you’ve ever felt intimidated by AI, start small. Start with math. One concept at a time.
This series is for learners like you—curious, determined, and ready to build something amazing.
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