Mastering AI: The Essential Prerequisites for Success
Understanding the foundational knowledge from Calculus to Probability needed to truly master AI and unlock its potential.
To really master AI and understand the techniques involved and to gain intuition, we need to master the prerequisite subject areas to gain the capability to not only understand and implement AI Algorithms, but also suggest improvements on existing implementations — Like Deepseek Implementing Reinforcement Learning on Llama.
These are the broad prerequisites one needs to master/ be good at building AI systems in the first place, or at the very least understand what is happening behind the scenes.
Mathematics: Linear Algebra, Calculus, and Probability & Statistics.
Programming: Python (and AI libraries like TensorFlow, PyTorch).
Data Science: Data preprocessing, feature engineering, and analysis.
Mathematics
Mathematics is important to master Artificial Intelligence, because at the end AI is all about maths — matrix operations, gradient calculations etc.., which we will see in action after mastering the prerequisites.
Linear Algebra
In Linear Algebra, we deal with vectors, matrices, and transformations which are essential for understanding the data structures in machine learning (models, weights, biases, layers, etc..).
Reference Texts:
“Linear Algebra and Its Applications” by Gilbert Strang
“Introduction to Linear Algebra” by Gilbert Strang
Calculus
In Calculus, we focus on derivatives, integrals, and optimization—important for training models and understanding gradients in learning algorithms, which forms the basis for techniques like back propagation.
Reference Texts:
“Calculus: Early Transcendentals” by James Stewart
“Calculus” by Michael Spivak
Probability & Statistics
Probability and Statistics are key to understanding data distributions, statistical inference, and modeling uncertainty in AI. This is also the core area necessary to perform data handling operations, transforming your data digestible by an AI Model.
But most of the books deal with the principles of statistics and probability, but not in the context of ML specifically. But nonetheless, they really prove themselves helpful in understanding techniques from optimization to metrics.
Reference Texts:
“An Introduction to Statistical Learning” by Trevor Hastie, Robert Tibshirani, Jerome Friedman
Programming
The most widely used language for AI development is Python, due to its simplicity and powerful libraries. There are other programming languages in the field of AI like Julia, but they are really not popular. Also it is a fact that you can do the same programming in other languages like Javascript, Java (Example: Tensorflow.js, Tensorflow Java), but python should always be your first call, primarily because the most important AI libraries (Tensorflow and Pytorch) are written with Python in mind, also it is really easy to build models in Python.
At some point, you have to choose between these two libraries (I generally prefer pytorch) we are going to make a comparison between the libraries in the future, to better understand the differences between the libraries.
Reference Texts: No reference texts recommended for Python. Follow through with the documentation and Youtube Tutorials to learn Python.
Data Science
Data Science is literally the field that deals with the study of data, like physics deals with the study of nature around us. Data Science is important to implement ML Algorithms because the data you’re given with to perform any ML Task is almost always unprepared (can also be unstructured too). The responsibility of cleaning and transforming data suitable for training your models (the final AI algorithms) requires a solid. understanding of Data Science.
Data Preprocessing: Involves cleaning and transforming raw data into a format suitable for model training.
- Text: “Python for Data Analysis” by Wes McKinney
Feature Engineering: The process of selecting and transforming features to improve model performance.
- Text: “Feature Engineering for Machine Learning” by Alice Zheng and Amanda Casari
What’s Next
In my next posts, I plan to cover a lot on the basics first, starting with Mathematics ( the concepts which we have either forgotten or have not implemented effectively to this day), I won’t be covering every concept, but will give insights into the tougher concepts out there related to ML.
Then we will dive into ML and Data Science starting with the basics to the advanced concepts and classes to understand currently implemented models, along with making our AI models to production.