My Machine Learning Projects Repository

Welcome to my Machine Learning Projects Repository! This collection is a reflection of my journey through the exciting world of machine learning and data science. Here, you'll find a variety of projects that I've worked on—some inspired by renowned resources and others born from my own curiosity.

What's Inside?

This repository encompasses a wide range of machine learning topics that I've explored:

• Supervised Learning:

  • Logistic Regression: Applying logistic regression techniques to predict real-world scenarios.
  • Support Vector Machines (SVM): Implementing SVMs for classification tasks.

• Unsupervised Learning:

  • Clustering Algorithms: Exploring K-means, hierarchical clustering, and DBSCAN.

• Natural Language Processing (NLP):

  • Text Representation:
    • Bag of Words and TF-IDF: Utilizing traditional methods for text vectorization.
    • Word Embeddings:
      • Word2Vec: Generating word embeddings to capture semantic relationships between words.
      • Doc2Vec: Extending word embeddings to represent documents.
  • Recurrent Neural Networks (RNN):
    • LSTM and GRU Networks: Building models for sequence prediction and language modeling.

• Deep Learning:

  • Convolutional Neural Networks (CNNs): Engaging in image classification and recognition projects.
  • Autoencoders: Implementing for dimensionality reduction and anomaly detection.

Project Origins

Many of these projects are inspired by the book Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron. This resource has been instrumental in guiding me through practical implementations of machine learning concepts.

In addition to these, you'll find projects that I've developed independently, driven by my passion to apply machine learning techniques to solve intriguing problems. my_personal_projects

Purpose of This Repository

This repository serves a dual purpose:

1. Personal Learning and Development: Working on these projects has deepened my understanding of machine learning algorithms, enhanced my coding skills, and honed my analytical thinking.

2. Community Sharing and Collaboration: By sharing my work, I aim to contribute to the learning journey of others interested in machine learning. Whether you're a seasoned data scientist, a student, or simply curious, I hope these projects provide valuable insights and inspire further exploration.

Certifications

To formalize my knowledge and stay updated with the latest advancements in data science, I've completed several certifications:

Feel free to check out these certifications to understand my formal training in the field.

Get Involved

I welcome feedback, discussions, and collaboration opportunities. If you have any questions or suggestions, please feel free to reach out. Let's learn and grow together!