> For the complete documentation index, see [llms.txt](https://hasithz.gitbook.io/hasith-abayakoon/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://hasithz.gitbook.io/hasith-abayakoon/certificates.md). # Certificates ## Specializations
Machine Learning Engineering for Production (MLOps) :link: [certificate](https://coursera.org/share/23f15ce39ed3bf07bfe7a2dbea124643) *** ### WHAT I LEARN * Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements. * Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application. * Build data pipelines by gathering, cleaning, and validating datasets. Establish data lifecycle by using data lineage and provenance metadata tools. * Apply best practices and progressive delivery techniques to maintain and monitor a continuously operating production system. *** ### SKILLS GAIN * Model Pipelines * Deployment Pipelines * Machine Learning Engineering for Production * Managing Machine Learning Production Systems * Data Pipelines
## Courses
Introduction to DevOps :link: [certificate ](https://coursera.org/share/54a72097b6090a2751455c648bbd2c74) ### What I learn * The essential characteristics of DevOps including building a culture of shared responsibility, transparency, and embracing failure. * The importance of Continuous Integration and Continuous Delivery, Infrastructure as Code, Test Driven Development, Behavior Driven Development. * Essential DevOps concepts: software engineering practices, cloud native microservices, automated continuous deployments, and building resilient code. * The organizational impact of DevOps, including breaking down silos, working in cross functional teams, and sharing responsibilities. *** ### Skills you will gain * DevOps * Software Development * Software Engineering * Cloud Infrastructure * Cloud Computing * Cloud Engineering * Software Testing * Continuous Deployment * Cloud-Native Computing * Behavior-Driven Development * IT Automation * Agile Software Development
Python Project for Data Engineering - IBM :link: [certificate](https://coursera.org/share/9e05547097b8b0a42173473a80b8e180) *** ### WHAT I LEARN * Demonstrate your skills in Python for working with and manipulating data * Implement webscraping and use APIs to extract data with Python * Play the role of a Data Engineer working on a real project to extract, transform, and load data * Use Jupyter notebooks and IDEs to complete your projec *** ### SKILL I GAIN * Python Programming * Information Engineering * Extract Transform and Load (ETL) * Data Engineer * Web Scraping
Python Project for Data Science - IBM :link: [certificate](https://coursera.org/share/e1408014e025efb306b694eca1fb8c60) ### WHAT I LEARN * Play the role of a Data Scientist / Data Analyst working on a real project. * Demonstrate your Skills in Python - the language of choice for Data Science and Data Analysis. * Apply Python fundamentals, Python data structures, and working with data in Python. * Build a dashboard using Python and libraries like Pandas, Beautiful Soup and Plotly using Jupyter notebook. *** ### SKILLS I GAIN * Data Science * Data Analysis * Python Programming * Pandas * Jupyter notebooks
Machine Learning Modeling Pipelines in Production :link: [certificate](https://coursera.org/share/48fcf126679df04d1ee2872fc0d12360) *** ### WHAT I LEARN * Apply techniques to manage modeling resources and best serve batch and real-time inference requests. * Use analytics to address model fairness, explainability issues, and mitigate bottlenecks. *** ### SKILLS I GAIN * Model Performance Analysis * Precomputing Predictions * Fairness Indicators * Explainable AI * autom
Introduction to Computer Vision and Image Processing - IMB :link: [certificate](https://coursera.org/share/997d5e1c447eb71ddcaa76a55691d457) *** ### WHAT I LEARN * Describe the applications of computer vision across different industries. * Apply image processing and analysis techniques to computer vision problems. * Utilize Python, Pillow, and OpenCV for basic image processing and perform image classification and object detection. * Create an image classifier using Supervised learning techniques. *** ### SKILLS I GAIN * Image Processing * Artificial Intelligence (AI) * Opencv * Computer Vision * Deep Learning
What is Data Science? - IBM :link: [certificate](https://coursera.org/share/f04f2deacac9c41a73aff472a331da78) *** ### WHAT YOU WILL LEARN * Define data science and its importance in today’s data-driven world. * Describe the various paths that can lead to a career in data science. * Summarize advice given by seasoned data science professionals to data scientists who are just starting out. * Explain why data science is considered the most in-demand job in the 21st century. *** ### SKILLS YOU WILL GAIN * Data Science * Big Data * Machine Learning * Deep Learning * Data Mining
Deploying Machine Learning Models in Production :link: [certificate](https://coursera.org/share/29fe70bb31c166ea8b11ec778e0ff07e) *** ### SKILLS I GAIN * Model Registries * TensorFlow Serving * Generate Data Protection Regulation (GDPR) * Model Monitoring * Machine Learning Operations (MLOps)
Introduction to Data Engineering - IBM :link: [certificate](https://coursera.org/share/697f34167c83ff1c21ef4f2438a298a7) *** ### WHAT I LEARN * List basic skills required for an entry-level data engineering role. * Discuss various stages and concepts in the data engineering lifecycle. * Describe data engineering technologies such as Relational Databases, NoSQL Data Stores, and Big Data Engines. *** ### SKILLS I GAIN * Data Science * Database (DBMS) * Information Engineering * SQL * NoSQL
Python for Data Science, AI & Development - IBM :link: [certificate](https://coursera.org/share/09adc8fb60581c853d5d1f31abb7daeb) *** ### WHAT I LEARN * Learn Python - the most popular programming language and for Data Science and Software Development. * Apply Python programming logic Variables, Data Structures, Branching, Loops, Functions, Objects & Classes. * Demonstrate proficiency in using Python libraries such as Pandas & Numpy, and developing code using Jupyter Notebooks. * Access and web scrape data using APIs and Python libraries like Beautiful Soup. *** ### SKILLS I GAIN * Data Science * Data Analysis * Python Programming * Numpy * Pandas
Machine Learning Data Lifecycle in Production :link: [certificate](https://coursera.org/share/3cfd0782c5130e2e3c9fc7a86dd84f50) *** ### WHAT I LEARN * Identify responsible data collection for building a fair ML production system. * Implement feature engineering, transformation, and selection with TensorFlow Extended * Understand the data journey over a production system’s lifecycle and leverage ML metadata and enterprise schemas to address quickly evolving data. *** ### SKILLS I GAIN * Convolutional Neural Network * Data Validation * ML Metadata * Data transformation * TensorFlow Extended (TFX)
Deep Neural Networks with PyTorch -IBM :link: [certificate](https://coursera.org/share/ed858b873ea7d4fba861cbff320c6387) *** ### WHAT I LEARN * Demonstrate your comprehension of deep learning algorithims and implement them using Pytorch. * Explain and apply knowledge of Deep Neural Networks and related machine learning methods. * Describe how to use Python libraries such as PyTorch for Deep Learning applications. * Build Deep Neural Networks using PyTorch. *** ### SKILLS I GAIN * Neural Networks * Python Programming * Numpy * PyTorch * Torch Vision * Model development
Introduction to Machine Learning in Production :link: [certificate](https://coursera.org/share/5eec39e4ce7d8d1f9f056c9014dbb187) *** ### WHAT I LEARN * Identify the key components of the ML lifecycle and pipeline and compare the ML modeling iterative cycle with the ML product deployment cycle. * Understand how performance on a small set of disproportionately important examples may be more crucial than performance on the majority of examples. * Solve problems for structured, unstructured, small, and big data. Understand why label consistency is essential and how you can improve it. *** ### SKILLS I GAIN * Concept Drift * ML Deployment Challenges * Human-level Performance (HLP) * Project Scoping and Design * Model baseline