Welcome to my AI/ML Learning Journey!
This repository documents my step-by-step learning of Artificial Intelligence (AI) and Machine Learning (ML) — from Python basics to advance-level ML models.
I’ll be updating this repo weekly with notes, exercises, and mini-projects.
- Build a strong foundation in Python, Math, and Statistics.
- Learn core ML algorithms and concepts.
- Apply knowledge through mini-projects and exercises.
- Document progress for GitHub & LinkedIn sharing.
- Python basics:
print, variables, loops, functions - Lists, dictionaries, tuples
- Simple class (OOP intro)
- Exercise: Mean, Median, Mode calculation (without libraries)
- Some tweaks with data and all
📂 Folder:Week01_Basics
- Matplotlib & Seaborn for plots
- Probability distributions, correlation, covariance
- EDA (Exploratory Data Analysis)
📂 Folder:Week02_DataViz_Stats
- Linear Regression, Cost function, Gradient Descent
- Evaluation metrics: R², MSE, MAE
- Mini-project: House Price Prediction
📂 Folder:Week03_Regression
- Logistic Regression, Decision Boundary
- Train/Test split, Confusion Matrix, Precision, Recall, F1 Score
- Mini-project: Spam Email Classifier
📂 Folder:Week04_Classification
- Python 3.x
- Jupyter Notebook
- Libraries:
NumPy,Pandas,Matplotlib,Seaborn,scikit-learn
- Navigate to the week folder you want to study.
- Open the Jupyter Notebook (.ipynb) for exercises and code examples.
- Read the
notes.mdfor quick explanations and theory. - Modify the code and experiment to deepen understanding!
I’ll post weekly updates on LinkedIn and link this repo.
Stay tuned for practical projects, small ML applications, and progress updates.
“Learning AI/ML is a marathon, not a sprint. Step by step, one notebook at a time!”