Machine Learning Engineering for Production (MLOps) Specialization

Issuer: DeepLearning.AI, Stanford University | Issued: Dec 2023 | Certificate

The MLOps Specialization teaches how to build and maintain integrated systems for continuous production, emphasizing efficient handling of evolving data. Explore tools and methodologies to master machine learning engineering, preparing usto contribute to cutting-edge AI solutions for real-world challenges.

Skills:

• Data Pipelines, Model Pipelines, Deployment Pipelines
• Machine Learning Engineering for Production
• Managing Machine Learning Production Systems

What I Learned:

• 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.

Machine Learning Specialization

Issuer: DeepLearning.AI, Stanford University | Issued: Oct 2023 | Certificate

The Machine Learning Specialization, a collaboration between DeepLearning.AI and Stanford Online, led by AI visionary Andrew Ng, provides foundational machine learning knowledge. This 3-course program covers key concepts, including supervised and unsupervised learning, offering practical skills for real-world applications.

Skills:

• Linear Regression, Logistic Regression, Decision Trees
• Artificial Neural Network
• Recommender Systems

What I Learned:

• Build ML models with NumPy & scikit-learn, create & train supervised models for prediction & binary classification tasks (linear, logistic regression)
• Build and train a neural network with TensorFlow for multi-class classification
• Apply best practices for machine learning development for models to generalize to real-world data and tasks
• Build and use decision trees and tree ensemble methods, including random forests and boosted trees
• Use unsupervised learning techniques for clustering and anomaly detection
• Build recommender systems with a collaborative filtering approach and a content-based deep learning method
• Build a deep reinforcement learning model

Practical Data Science on the AWS Cloud Specialization

Issuer: DeepLearning.AI, Stanford University | Issued: Nov 2023 | Certificate

The Practical Data Science Specialization equips Python and SQL proficient developers, scientists, and analysts with skills for deploying scalable ML projects using Amazon SageMaker in the AWS cloud. This program integrates domain knowledge, mathematics, statistics, data visualization, and programming to build, train, and deploy end-to-end ML pipelines. With hands-on labs, I tackled challenges using state-of-the-art algorithms like BERT and FastText for NLP and NLU. Learned to overcome obstacles in moving projects from idea to deployment swiftly, handling massive datasets, tuning models, and managing deployments for thousands of models or more. Mastered the essential skills for transforming ideas into scalable, operational solutions.

Skills:

• Data Labeling at Scale, ML Pipelines and MLOps
• Automated Machine Learning (AutoML)
• A/B Testing and Model Deployment
• Amazon SageMaker, Amazon Glue, Amazon Athena, Amazon EMR
• Natural Language Processing with BERT, Text Transformation

What I Learned:

• Prepare data, detect statistical data biases, perform feature engineering at scale to train models, and train, evaluate, and tune models with AutoML
• Store and manage ML features using a feature store, and debug, profile, tune, and evaluate models while tracking data lineage and model artifacts
• Build, deploy, monitor, and operationalize end-to-end machine learning pipelines
• Build data labeling and human-in-the-loop pipelines to improve model performance with human intelligence

Big Data Specialization

Issuer: University of California San Diego | Issued: Sep 2023 | Certificate

Skills:

• SQL, Big Data, Mongodb, Neo4j
• Hadoop Ecosystem, Apache Spark
• Distributed Computing Architecture

NoSQL, Big Data, and Spark Foundations Specialization

IBM | Issued: Dec 2023 | Certificate

This certificate signifies mastery in NoSQL and Big Data, encompassing CRUD operations in MongoDB, IBM Cloudant, and Apache Cassandra. Proficiency extends to foundational knowledge of Apache Hadoop, Hive, HDFS, MapReduce, Apache Spark, Spark SQL, Spark Structured Streaming, Spark ML, scaling Spark with Kubernetes, and Monitoring. The learner gained hands-on skills, completing a project involving ETL processing and Machine Learning model deployment using Spark. Now equipped for essential big data management tasks and challenges with NoSQL and Spark expertise.

Skills:

• Big Data, Cloud Database
• Mongodb, Cassandra, Cloudant
• Machine Learning Pipelines
• Data Engineer
• Apache Spark, SparkML, SparkSQL, Apache Hadoop

What I Learned:

• Work with NoSQL databases to insert, update, delete, query, index, aggregate, and shard/partition data.
• Develop hands-on NoSQL experience working with MongoDB, Apache Cassandra, and IBM Cloudant.
• Develop foundational knowledge of Big Data and gain hands-on lab experience using Apache Hadoop, MapReduce, Apache Spark, Spark SQL, and Kubernetes.
• Perform Extract, Transform and Load (ETL) processing and Machine Learning model training and deployment with Apache Spark.

Google Data Analytics Certificate

Issuer: Google Career Certificates | Issued: Aug 2023 | Certificate

Skills:

• Data Collection, Data Cleansing, Data Visualization
• Data Integrity, Data Aggregation, Data Calculations, Data Ethics
• Spreadsheet, Metadata, SQL
• R Markdown, R Programming, Rstudio, Tableau Software
• Creating case studies, Data Analysis, Presentation
• Sample Size Determination, Decision-Making
• Developing a portfolio, Problem Solving, Questioning

What I Learned:

• Gained an immersive understanding of the practices and processes used by a junior or associate data analyst in their day-to-day job
• Learned key analytical skills (data cleaning, analysis, & visualization) and tools (spreadsheets, SQL, R programming, Tableau)
• Understand how to clean and organize data for analysis, and complete analysis and calculations using spreadsheets, SQL and R programming
• Learned how to visualize and present data findings in dashboards, presentations and commonly used visualization platforms