๐ Master's (MSc) Journey in Computer Engineering and Automation (AI Focus) (in process) | Master in Data Science (Completed on 2022)
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Module 1: Python and Math ๐๐งฎ
- 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.
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Module 2: Scikit-Learn, ML Models, EDA, and Practice ๐ค๐
- 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.
- Module 3: Advanced ML with Scikit-Learn ๐๐คฏ
- 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.
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Module 5: Git, Automation, Intro to Deep Learning ๐๐ค
- 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.
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Module 6: DevOps for Deep Learning Projects โ๏ธ๐ง
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Modules 13-15: Advanced AI Projects with Web Application Integration and Arduino Robotics ๐๐ป๐ค
- 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.
- 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.
- 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.