leondoungala22/practical_ai_ml_dl_datascience_portfolio

AI / Machine Learning / Data Science - ( In process )

🚀 Master's (MSc) Journey in Computer Engineering and Automation (AI Focus) (in process) | Master in Data Science (Completed on 2022)

---

Personal Projects Implementation Odyssey

---

Foundations (Modules 1-2)

1. Module 1: Python and Math 🐍🧮

   • Content: Embark on a voyage to master Python fundamentals and mathematical concepts.
   • Skills: Attain proficiency in Python and establish a robust foundation in mathematics.
   • Resources: Coursera Python | Khan Academy - Math | NumPy Quickstart
   • Project: Craft a Python application intertwined with essential math concepts.

2. Module 2: Scikit-Learn, ML Models, EDA, and Practice 🤖📊

   • Content: Navigate the terrain of scikit-learn, basic ML models, exploratory data analysis (EDA), and hands-on projects.
   • Skills: Proficiency in EDA, grasp of basic ML algorithms.
   • Resources: Diverse online courses | Scikit-learn Documentation.
   • Models: Linear Regression, SVM, Decision Trees, Random Forest, and more.
   • Project: Apply ML models to a dataset, unveiling the power of EDA.

---

Advanced ML and Cloud Platforms (Modules 3-4)

3. Module 3: Advanced ML with Scikit-Learn 🚀🤯
   • Content: Delve into the intricacies of advanced ML algorithms, ensembles, and clustering.
   • Skills: Mastery of advanced ML techniques, ensemble models, and clustering strategies.
   • Resources: Coursera ML Specialization | PapersWithCode.
   • Project: Implement sophisticated ensemble models.

---

Automation, DevOps, and Deep Learning Basics (Modules 5-6)

4. Module 5: Git, Automation, Intro to Deep Learning 🔄🤖

   • Content: Command Git, explore automation, and dive into the realms of TensorFlow and PyTorch.
   • Skills: Proficiency in Git, automation for ML workflows, basic understanding of deep learning.
   • Resources: Git and GitHub Courses | TensorFlow Documentation | PyTorch Documentation.
   • Project: Establish Git for a deep learning project, culminating in a simple model.

5. Module 6: DevOps for Deep Learning Projects ⚙️🧠

   • Content: Implement DevOps practices tailored for deep learning projects, including CI/CD pipelines, infrastructure as code, and model deployment strategies.
   • Skills: Automated testing, containerization, orchestration, continuous integration, continuous deployment.
   • Resources: Jenkins Documentation | Docker Documentation | Kubernetes Documentation.
   • Project: Develop a CI/CD pipeline for a deep learning model, integrating automated testing, containerization, and deployment strategies.

---

Module 7: Advanced Automation and Deep Learning Fundamentals 🚀🤖📚

• Content: Navigate advanced automation, delve into PLC, and explore deeper into deep learning.
• Skills: Application of automation in ML workflows, understanding of PLC, experimentation with deep learning architectures.
• Resources: Advanced automation tools, PLC basics, and engaging online courses.
• Project: Implement an end-to-end ML pipeline.

---

Advanced Deep Learning and Computer Vision (Modules 7-8)

Module 8: Advanced Deep Learning Techniques 🌌💡

• Content: Explore the frontiers of advanced deep learning techniques, including transfer learning and optimization.
• Skills: Command over advanced deep learning concepts.
• Resources: Specialized courses and cutting-edge research articles.
• Project: Implement transfer learning on a pre-trained model.

Module 9: Computer Vision Basics and OpenCV 👁️🤖

• Content: Grasp the fundamentals of computer vision and master the art of OpenCV.
• Skills: Navigate the basics of computer vision and implement applications using OpenCV.
• Resources: Engaging online courses and comprehensive documentation.
• Project: Develop a computer vision application using OpenCV.

---

Module 10: Cloud Platforms for ML ☁️🧠

• Content: Harness the power of AWS, Azure, and GCP for ML, with a special focus on deployment.
• Skills: Proficiency in deploying ML models on cloud platforms.
• Resources: AWS Documentation | Azure AI Documentation | Google Cloud AI Documentation.
• Project: Deploy an ML model on AWS SageMaker.

---

Data Visualization (Module 8.1)

Module 8.1: Tableau and Power BI📊💡

• Content: Master the art of data visualization using Tableau and Power BI.
• Skills: Proficiency in creating insightful visualizations for effective data communication.
• Resources: Tableau Documentation | Power BI Documentation.
• Project: Create compelling visual narratives using Tableau and Power BI.

---

Advanced AI and Big Data (Modules 9-10)

Module 9: Advanced Generative AI and GANs 🎨🤖

• Content: Delve into the intricate world of advanced generative AI, exploring GANs and sequence models.
• Skills: Mastery of GANs and sequence models.
• Resources: Specialized courses and enlightening research articles.
• Project: Implement a GAN for awe-inspiring image generation.

Module 10: Introduction to Big Data in AI 📊🚀

• Content: Grasp the basics of Big Data in AI, with a focal point on Apache Spark and Hadoop.
• Skills: Mastery of Apache Spark and Hadoop.
• Resources: Apache Spark Documentation | Hadoop Tutorials.
• Project: Process and analyze colossal datasets using Apache Spark.

---

Reinforcement Learning and NLP (Modules 11-12)

Module 11: Model Evaluation, Hyperparameter Tuning, API Deployment 📈🛠️🚀

• Content: Embark on the journey of model evaluation, hyperparameter tuning, and NLP, with a sprinkle of API deployment.
• Skills: Command over model evaluation, NLP hyperparameter tuning, and exporting models as APIs.
• Resources: Enlightening articles on model optimization for NLP.
• Project: Evaluate and fine-tune a pre-trained GPT model, culminating in deployment as an API.

Module 12: AI Ethics, History, and Current Applications 🌐🤔🚀

• Content: Reflect on the ethical considerations in AI, journey through history, and explore current applications.
• Skills: Develop ethical decision-making skills and a profound historical understanding.
• Resources: Attend enlightening lectures and delve into articles on AI ethics.
• Project: Explore the ethical implications of a real-world AI application.

---

Advanced Projects (Modules 13-16)

Modules 13-15: Advanced AI Projects with Web Application Integration and Arduino Robotics 🚀💻🤖

• Content: Engage in advanced AI projects, emphasizing implementation, project management, web app integration, and Arduino robotics.
• Skills: Command over implementation, project management, model integration, and the fascinating realm of Arduino robotics.
• Resources: Cutting-edge research articles and participation in online conferences.
• Project: Undertake a substantial AI project, focusing on project management, implementation, integration, and the thrilling domain of robotics.

Module 16: Exploring Emerging AI Trends 🚀🌐

---

Additional Important Topics

• Reinforcement Learning: Basics and applications in AI.
• Quantum Computing and AI: Intersection of quantum computing and AI.
• Explainable AI (XAI): Techniques to make AI models interpretable.
• Robotic Process Automation (RPA): Automating rule-based tasks with software robots.
• AI in Healthcare: Applications in diagnostics, drug discovery, personalized medicine.
• Edge Computing and AI: Role of edge computing in real-time AI processing.
• AI Security and Ethics: Considerations in AI, including bias mitigation.

---

Personal Project Implementation (Throughout the Program)

• Continuous Learning: Engage in small personal projects regularly.
• GitHub Portfolio: Showcase project implementations, code, documentation.
• Networking: Connect with AI professionals, attend webinars, participate in communities.
• Technical Blogging: Document learning journey, project implementations through blog posts.
• Code Reviews: Actively participate in and conduct code reviews for collaboration and skill improvement.
• Hackathons and Competitions: Participate in AI-related events to test and improve skills.