Day-3/90 | AI, DS and ML complete course for beginners in English|Hire Ready| Project Workflow of AI

Welcome to Day 3 of your Complete Artificial Intelligence (AI), Data Science (DS), and Machine Learning (ML) learning journey in English. This in-depth tutorial walks you through the end-to-end project workflow used by data scientists and AI professionals to build production-ready ML models and data-driven solutions.

In this video, you will learn every critical step of AI, DS, and ML project execution, explained clearly for beginners and aspiring data scientists:

Problem Statement Definition: Discover how to articulate clear, measurable project goals aligned with business needs and domain challenges. Learn to research existing solutions and data availability to validate your problem’s feasibility.

Data Source Searching: Understand where and how to find quality datasets from various online repositories, APIs, databases, or simulate custom datasets for your project.

Data Collection: Master techniques for gathering data through web scraping, database queries, APIs, or integrating multiple data streams effectively.

Data Cleaning & Preprocessing: Learn best practices for handling missing data, eliminating duplicates, correcting errors, transforming variables, and formatting datasets for analysis.

Exploratory Data Analysis (EDA): Apply visualization techniques and statistical summaries to uncover hidden trends, anomalies, and feature relationships that inform modeling decisions.

Feature Engineering: Develop skills for creating new features, selecting important attributes, encoding categorical variables, and scaling numerical data to boost model accuracy and robustness.

Model Selection & Training: Explore the criteria for choosing appropriate ML algorithms based on problem type (classification, regression, clustering), dataset size, and complexity. Learn training protocols, cross-validation, and hyperparameter optimization.

Model Evaluation: Assess your model’s predictive power using relevant metrics such as accuracy, precision, recall, F1-score, AUC-ROC for classification, or RMSE for regression. Understand validation strategies and avoid overfitting.

Deployment: Learn how to package and deploy your trained models for real-world use via REST APIs, cloud platforms, or edge devices. Understand concepts of MLOps, versioning, and scalable architecture.

Documentation & Maintenance: Emphasize thorough documentation for reproducibility, tracking model performance over time, and scheduling retraining to adapt to evolving data distributions and concept drifts.

This comprehensive roadmap is supported with examples demonstrating practical use cases like customer churn prediction, sentiment analysis, fraud detection, and recommendation systems. The session also highlights Python’s rich ecosystem including Pandas, Scikit-learn, TensorFlow, and cloud deployment frameworks shaping modern AI pipelines.