Prerequisites

Math Foundations (Non-Negotiable)

Topics:

• Complex numbers (Euler’s formula, magnitude/phase)
• Linear algebra
• Vectors, matrices
• Dot products
• Eigenvalues (basic intuition)
• Probability
• Random variables
• Mean, variance
• Gaussian noise
• Fourier Transform
• Convolution
• Sampling theorem (Nyquist, aliasing)

Resources:

• 3Blue1Brown – Essence of Linear Algebra
• Khan Academy – Fourier Series & Transform
• MIT OCW – Signals & Systems (6.003)
• Better Explained – Fourier Transform

DSP

Topics

Discrete signals

FFT / DFT

FIR vs IIR filters

Filtering

Windowing

Decimation & interpolation

Spectrograms

Correlation

Noise & SNR

Resources

Book (best single source): Understanding Digital Signal Processing – Rick Lyons (Ch. 1–6, 10–12)

Free book: The Scientist and Engineer’s Guide to DSP https://www.dspguide.com/

MIT OCW – DSP (6.341) https://ocw.mit.edu/courses/6-341-discrete-time-signal-processing-fall-2005/

FFT Visualization: https://betterexplained.com/articles/an-interactive-guide-to-the-fourier-transform/

3️⃣ RF & SDR Basics (You Must Understand the Hardware Reality)

This prevents you from building fragile ML models.

Topics

IQ sampling

Mixers & heterodyning

Local oscillators

Phase noise

ADC resolution

Dynamic range

Noise figure

Antennas basics

IQ imbalance & DC offset

Resources

RTL-SDR Blog Tutorials https://www.rtl-sdr.com/start-here/

Keysight RF Basics (YouTube)

Book (select chapters): RF Microelectronics – Behzad Razavi

ARRL Handbook (RF sections)

4️⃣ Python for DSP & ML (Absolute Must)

Track C is Python-heavy.

Topics

NumPy arrays

Broadcasting

FFT in NumPy

Matplotlib plotting

SciPy filters

File I/O for IQ data

Resources

NumPy Quickstart https://numpy.org/doc/stable/user/quickstart.html

SciPy Signal Docs https://docs.scipy.org/doc/scipy/reference/signal.html

Matplotlib Tutorial https://matplotlib.org/stable/tutorials/introductory/pyplot.html

5️⃣ Machine Learning Fundamentals

You don’t need to be a Kaggle god, but you must understand ML mechanics.

Topics

Train/test splits

Overfitting

Loss functions

Gradient descent

Classification metrics

Confusion matrix

Feature scaling

Hyperparameter tuning

Resources

Coursera – Andrew Ng ML (Weeks 1–6)

scikit-learn Tutorial https://scikit-learn.org/stable/tutorial/basic/tutorial.html

StatQuest (YouTube):

Logistic regression

Random forests

Neural networks

6️⃣ Deep Learning (Minimal Gate)

You need just enough DL to not drown later.

Topics

Feedforward networks

CNN basics

Backpropagation

Optimizers (Adam, SGD)

Regularization

Batch normalization

Data loaders

Resources

PyTorch 60-Minute Blitz https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html

Fast.ai Practical DL (Lessons 1–4)

Coursera DL Specialization (Course 1)

7️⃣ GNU Radio / SDR Tooling (Operational Literacy)

You must be able to touch RF.

Topics

Installing GNU Radio

Flowgraphs

RTL-SDR drivers

Capturing IQ

Basic demodulators

SoapySDR

Resources

GNU Radio Tutorials https://wiki.gnuradio.org/index.php/Tutorials

SDR++ https://www.sdrpp.org/

Great Scott Gadgets SDR Tutorials

RTL-SDR Quick Start https://www.rtl-sdr.com/qsg/

✅ Minimal Gate Checklist (If You Can Do This, You’re Ready)

You’re Track-C-ready if you can:

Plot FFT of an IQ file

Design a bandpass filter

Compute SNR

Load RadioML dataset

Train a simple CNN

Capture IQ from RTL-SDR

Demodulate FM

Build a spectrogram

Train a random forest classifier