This repository contains my completed programming assignments and guided projects from various Coursera specializations.
Much of my journey started with Andrew Ng’s courses, which got me interested in machine learning and deep learning. Since then, I’ve continued learning through DeepLearning.AI and other programs, covering machine learning, deep learning, NLP, TensorFlow, and data science.
| Specialization | Organization | Topics |
|---|---|---|
| Machine Learning (Stanford) | Stanford / Andrew Ng | Linear regression, Logistic regression, SVMs |
| Machine Learning Specialization | DeepLearning.AI | Updated Version of the Course |
| Deep Learning Specialization | DeepLearning.AI | Neural networks, CNNs, RNNs, Sequence Models |
| DeepLearning.AI TensorFlow Developer | DeepLearning.AI | TensorFlow, CNNs, NLP, TF Serving |
| TensorFlow: Advanced Techniques | DeepLearning.AI | Custom models, TF APIs, Sequence-to-sequence |
| IBM Data Science Professional Certificate | IBM | Python, Data analysis, SQL, Visualization |
| Natural Language Processing | DeepLearning.AI | Word embeddings, RNNs, Seq2Seq, Attention |
| Guided Projects | Coursera | Hands-on short ML/DS tasks |
- Python, Jupyter Notebook, Google Colab, MATLAB/Octave
- TensorFlow, Keras, PyTorch (minor)
- NumPy, Pandas, Scikit-learn, Matplotlib
- Building & training neural networks from scratch
- Applying CNNs, RNNs, LSTMs, and Attention
- Text classification & machine translation
- Data preprocessing, feature engineering
- ML fundamentals: regression, classification, clustering
These are personal solutions for learning purposes.
Please use them responsibly and avoid submitting as your own coursework.