100 Days of ML for Geotechnical Engineering

From Python Basics to AI-Powered Geotechnical Solutions

Author	Ripon Chandra Malo
Affiliation	PhD Student — Civil & Environmental Engineering (Geotechnical), University of Utah
Research	Granular Materials x Artificial Intelligence
Start Date	February 2026

---

About This Repository

A structured 100-day journey bridging the gap between classical geotechnical engineering and modern machine learning / deep learning — one commit, one notebook, one day at a time.

Every day includes a Python script (.py), a Jupyter Notebook (.ipynb), concise notes (notes.md),.

The journey starts from Python fundamentals and progresses to Physics-Informed Neural Networks (PINNs), LSTMs for seismic data, and end-to-end geotechnical ML pipelines.

---

Repository Structure

100-Days-of-ML-for-Geotechnical-Engineering/
│
├── README.md
├── LICENSE
├── requirements.txt
├── .gitignore
│
├── Phase-1_Python-Fundamentals/          (Day 001 – 015)
├── Phase-2_Data-Science-Essentials/      (Day 016 – 035)
├── Phase-3_Classical-ML/                 (Day 036 – 060)
├── Phase-4_Deep-Learning/                (Day 061 – 085)
├── Phase-5_Geotechnical-AI-Projects/     (Day 086 – 100)
│
└── assets/

---

Daily Workflow

Step	Time	Action
Learn	10 min	Read / watch the concept
Code	35 min	Implement in .ipynb / .py
Notes	10 min	Write notes.md with key takeaways
Push	5 min	git add > commit > push + Upload .ipynb to Kaggle

---

Phase Overview

Phase	Days	Focus	Status
Phase 1	001 – 015	Python Fundamentals
Phase 2	016 – 035	Data Science Essentials
Phase 3	036 – 060	Classical Machine Learning
Phase 4	061 – 085	Deep Learning (PyTorch, PINNs)
Phase 5	086 – 100	Geotechnical AI Capstone Projects

Legend:      

---

100-Day Progress Tracker

Day 001 – 015

Day	Topic	Notebook	Status
001	Variables, Data Types & Type Casting	Notebook
002	Conditionals — if / elif / else	Notebook
003	Loops — for, while, enumerate, zip	Notebook
004	Strings & String Methods	Notebook
005	Lists, Tuples & Sets	Notebook
006	Dictionaries & Comprehensions	Notebook
007	Functions & Lambda Expressions	Notebook
008	File I/O — Read & Write CSV, TXT	Notebook
009	Error Handling — try / except / finally	Notebook
010	OOP — Classes & Objects	Notebook
011	OOP — Inheritance & Polymorphism	Notebook
012	Modules & Packages	Notebook
013	Decorators & Generators	Notebook
014	Regular Expressions (re module)	Notebook
015	Mini Project: USCS Soil Classification Tool	Notebook

Day 016 – 035

Day	Topic	Notebook	Status
016	NumPy — Arrays & Operations	Notebook
017	NumPy — Linear Algebra (SVD, Eigenvalues)	Notebook
018	NumPy — Signal Processing Basics	Notebook
019	Pandas — Series & DataFrames	Notebook
020	Pandas — Data Cleaning & Missing Values	Notebook
021	Pandas — GroupBy & Aggregation	Notebook
022	Pandas — Merge, Join & Concat	Notebook
023	Matplotlib — Basic Plots	Notebook
024	Matplotlib — Subplots & Customization	Notebook
025	Seaborn — Statistical Visualization	Notebook
026	Plotly — Interactive Plots	Notebook
027	SciPy — Optimization	Notebook
028	SciPy — Interpolation & Curve Fitting	Notebook
029	SciPy — Signal Processing (FFT, Filtering)	Notebook
030	Exploratory Data Analysis (EDA) Workflow	Notebook
031	Feature Engineering Techniques	Notebook
032	Data Preprocessing Pipeline	Notebook
033	Handling Imbalanced Datasets	Notebook
034	Dimensionality Reduction (PCA, SVD)	Notebook
035	Mini Project: SPT Borehole Data EDA	Notebook

Day 036 – 060

Day	Topic	Notebook	Status
036	Linear Regression from Scratch	Notebook
037	Linear Regression with Scikit-Learn	Notebook
038	Polynomial, Ridge & Lasso Regression	Notebook
039	Logistic Regression	Notebook
040	K-Nearest Neighbors (KNN)	Notebook
041	Decision Trees	Notebook
042	Random Forest	Notebook
043	Gradient Boosting & XGBoost	Notebook
044	Support Vector Machines (SVM)	Notebook
045	Naive Bayes Classifier	Notebook
046	K-Means Clustering	Notebook
047	DBSCAN & Hierarchical Clustering	Notebook
048	Cross-Validation & Hyperparameter Tuning	Notebook
049	Grid Search vs Random Search vs Optuna	Notebook
050	Regression Metrics (R-squared, RMSE, MAE)	Notebook
051	Classification Metrics (ROC, AUC, F1)	Notebook
052	Ensemble Methods Deep Dive	Notebook
053	Feature Importance & SHAP Explainability	Notebook
054	ML Pipeline with Scikit-Learn	Notebook
055	Soil Liquefaction Prediction (ML)	Notebook
056	Shear Wave Velocity from SPT (ML)	Notebook
057	Bearing Capacity Prediction (ML)	Notebook
058	Soil Type Classification (ML)	Notebook
059	CPT Data Interpretation with ML	Notebook
060	Mini Project: Full Geotech ML Pipeline	Notebook

Day 061 – 085

Day	Topic	Notebook	Status
061	Neural Network from Scratch (NumPy)	Notebook
062	Intro to PyTorch — Tensors	Notebook
063	PyTorch — Autograd & Backpropagation	Notebook
064	PyTorch — Building a Simple NN	Notebook
065	Activation Functions Deep Dive	Notebook
066	Loss Functions & Optimizers	Notebook
067	Regularization (Dropout, BatchNorm)	Notebook
068	CNN — Fundamentals	Notebook
069	CNN — Image Classification	Notebook
070	Transfer Learning (ResNet, VGG)	Notebook
071	RNN — Fundamentals	Notebook
072	LSTM & GRU Networks	Notebook
073	Time Series Forecasting with LSTM	Notebook
074	Autoencoders	Notebook
075	Variational Autoencoders (VAE)	Notebook
076	Intro to GANs	Notebook
077	Physics-Informed Neural Networks (PINNs)	Notebook
078	PINNs for Geotechnical Problems	Notebook
079	Graph Neural Networks Basics	Notebook
080	Attention Mechanism & Transformers Intro	Notebook
081	Tabular Deep Learning (TabNet)	Notebook
082	Hyperparameter Tuning for Deep Learning	Notebook
083	Model Deployment (ONNX, TorchScript)	Notebook
084	Seismic Signal Classification (DL)	Notebook
085	Ground Motion Prediction with LSTM	Notebook

Day 086 – 100

Day	Topic	Notebook	Status
086	SPT to Shear Wave Velocity (ANN + Ensemble)	Notebook
087	Liquefaction Susceptibility Mapping (DL)	Notebook
088	Soil Classification from CPT using CNN	Notebook
089	Settlement Prediction with ML	Notebook
090	Seismic Response Spectra Prediction	Notebook
091	Retaining Wall Design Optimization	Notebook
092	Slope Stability with ML	Notebook
093	Ground Motion Selection using Clustering	Notebook
094	DIGGS Data Automated Analysis Pipeline	Notebook
095	Reduced-Order Modeling for Geotechnics	Notebook
096	Streamlit Dashboard for Geotech ML	Notebook
097	Full Project: End-to-End ML Application	Notebook
098	Documentation & README Polish	Notebook
099	Kaggle Competition Submission	Notebook
100	Portfolio Summary & Reflection	Notebook

---

Tech Stack

Category	Tools
Language	Python 3.10+
Data	NumPy, Pandas, SciPy
Visualization	Matplotlib, Seaborn, Plotly
ML	Scikit-Learn, XGBoost, Optuna
Deep Learning	PyTorch
Deployment	Streamlit, ONNX
Environment	Jupyter Notebook, VS Code
Version Control	Git & GitHub
Notebooks	Kaggle Kernels

---

How to Use

# Clone the repository
git clone https://github.com/YOUR_USERNAME/100-Days-of-ML-for-Geotechnical-Engineering.git
cd 100-Days-of-ML-for-Geotechnical-Engineering

# Install dependencies
pip install -r requirements.txt

# Open any day's notebook
jupyter notebook Phase-1_Python-Fundamentals/Day_001/day_001.ipynb

---

Connect With Me

---

License

This project is licensed under the MIT License — see the LICENSE file for details.

---

---

Note

If you find this repository useful, consider giving it a star.

---

"The best time to plant a tree was 20 years ago. The second best time is now."

— Chinese Proverb