Model Development (15hrs)
Model development in machine learning involves several stages: defining the problem, collecting and preprocessing data, and conducting exploratory data analysis (EDA) to understand patterns. Feature engineering enhances predictive power by creating new features. Models are then selected, trained, and optimized using various algorithms and hyperparameter tuning. Evaluation metrics such as accuracy, precision, and recall assess model performance. Once a satisfactory model is chosen, it is deployed into production, integrated with systems, and continuously monitored for performance. Effective model development combines technical expertise and domain knowledge to create models that deliver actionable insights and add value.
Our mission
To develop innovative, reliable, and efficient machine learning models that leverage data-driven insights to solve complex problems, enhance decision-making, and drive transformative growth for businesses and society.
Our vision
To be a leader in machine learning model development, continuously pushing the boundaries of technology to create intelligent solutions that empower organizations worldwide, fostering a future where data and AI work harmoniously to improve lives and drive sustainable progress.
Content
Day 1: Introduction to Machine Learning
Introduction to Machine Learning
What is Machine Learning?
How AI, ML, and Deep Learning Differ?
Different Domains of Machine Learning
Introduction to Generative AI
Why Machine Learning?
Converting Concepts to Projects
ML Algorithms Knowledge(concept)
Support Vector Machine (SVM)
Linear Regression
Logistic Regression
Ensemble Learning
Random Forest
Catboost
XGBoost
Practical Knowledge and Technology
Data Handling: Data everywhere, but no data to use directly
Feature Creation: How features are exactly created
Model Development: Developing a model from scratch to production
Day 2-3: Algorithms, Technology, and Tools
Algorithms and Industrial Knowledge
Turning Random Data into ML Dataset
Making Code Production Ready
Developing Models from Scratch to Production
Creating APIs for Models
Cloud Storage: AWS
Hosting ML on Cloud (GitLab)
Technology Used
Programming Languages: Python
SQL Databases: Redshift
Cloud Services: AWS - Jupyter Notebook, S3, Glue, Athena, Crawler
Ticketing Tools: Jira
Version Control: GitLab
Database Management: Redis
Day 4: ML-Ops and End-to-End Project Life Cycle
ML-Ops and Pushing Models to Production
Overview of ML-Ops
Real-time Project Implementation
Scalability of ML Projects
End-to-End ML Project Life Cycle
Introduction to End-to-End ML Project Life Cycle
Preprocessing of Data
Feature Creation
Algorithm Implementation
Evaluation Methods
Technology Integration
Storage: AWS (S3, Athena, Glue, Crawler, Bucket)
Database: SQL, Redshift, Redis
Model Development: Jupyter Notebook
Testing Methods: A/B Testing, Unit Test, Integration Test, Regression Test, Invariance Test, Directional Expectation Test, Minimum Functionality Test
Extra: Practical Projects and Ideas
Creating Storage Pipelines
Testing Model Frameworks
Cool Project Ideas for Implementation
Dataset Library and Useful Links
Developing Models on Cloud
Creating Pipelines from Random Data to Model Output on Cloud