A curated collection of Jupyter notebooks and Python scripts covering essential concepts in machine learning, deep learning, and natural language processing (NLP). This repository serves as a learning and reference resource for both beginners and experienced practitioners.
This repository contains Jupyter notebooks and experiments across a range of topics:
- Exploratory Data Analysis (EDA)
- Supervised Learning (regression & classification)
- Unsupervised Learning (clustering, dimensionality reduction)
- Deep Learning with TensorFlow/Keras and PyTorch
- Natural Language Processing using NLTK, spaCy, and Transformers
- Real-world projects (image classification, stock market prediction, NLP tasks)
These notebooks are great for self-study, bootcamp preparation, or building end-to-end machine learning pipelines.
- Data cleaning and manipulation with Pandas
- Visualization with Matplotlib and Seaborn
- Regression (Linear, Logistic)
- Decision Trees, Random Forest, XGBoost
- Feature selection and preprocessing
- Neural networks using TensorFlow/Keras and PyTorch
- Image classification with CNNs
- Transfer learning
- RNNs and LSTMs for sequence modeling
- Text preprocessing with NLTK and spaCy
- Word embeddings (Word2Vec, GloVe)
- Transformer-based models using Hugging Face Transformers
- Fine-tuning BERT for classification and generation
- Text classification, sentiment analysis, and NER
You will need Python 3.8+ and the following libraries:
- pandas
- numpy
- matplotlib
- seaborn
- scikit-learn
- tensorflow
- keras
- torch
- nltk
- spacy
- transformers
Clone the repository:
git clone https://github.com/syedabdullahbukhari77/MLE_DL_Notebooks.git
cd MLE_DL_Notebooks