Data Science & AI Mastery: From Basics to Deployment

Description

What you’ll learn

Clean, analyze, and prepare data for real-world machine learning and AI applications.
Build and evaluate machine learning models for regression, classification, clustering, and recommendation systems.
Apply deep learning techniques such as neural networks, CNNs, RNNs, and generative AI for advanced use cases.
Work confidently with Python, Pandas, NumPy, Scikit-Learn, TensorFlow, and PyTorch to solve end-to-end problems
Perform feature engineering, model optimization, and hyperparameter tuning to improve accuracy
Deploy models into production using APIs (FastAPI, Flask), Docker, and Streamlit dashboards.
Understand the basics of MLOps, including model monitoring, performance tracking, and drift detection.
Tackle real-world projects and a capstone, gaining the confidence to showcase a complete portfolio to employers.
Translate technical outputs into business insights and decision-making strategies.
Prepare for career roles like Data Scientist, Machine Learning Engineer, or AI Specialist.

“This course contains the use of artificial intelligence.”

This comprehensive Data Science and Artificial Intelligence Mastery course is designed to take you from beginner to job-ready professional in just 100 days. Through a carefully structured curriculum, you’ll gain both theoretical knowledge and hands-on experience with the most in-demand tools and technologies in the industry.

You’ll begin by building a strong foundation in data analysis, data cleaning, and feature engineering, learning how to work with structured and unstructured data. From there, you’ll dive deep into machine learning algorithms such as regression, classification, and clustering, while also mastering advanced topics like deep learning, neural networks, and generative AI.

Every step of the way, you’ll reinforce your skills with hands-on labs, real-world case studies, and a capstone project that simulates industry challenges. You’ll also explore data visualization, model deployment with APIs (FastAPI, Flask), and MLOps concepts like monitoring and drift detection, preparing you for the realities of production environments.

By the end of this course, you’ll have a polished portfolio showcasing end-to-end AI projects, a deep understanding of tools such as Python, Pandas, Scikit-Learn, TensorFlow, PyTorch, Docker, and Streamlit, and the confidence to apply for roles like Data Scientist, Machine Learning Engineer, or AI Specialist.

This isn’t just a course—it’s a complete career preparation journey, giving you the skills, projects, and confidence to stand out in today’s competitive data-driven job market.

Who this course is for:

Beginners in Data Science & AI who want a structured, step-by-step path to mastery.
Students and recent graduates looking to gain practical skills and build a strong project portfolio.
Professionals in IT, software engineering, or business analytics who want to transition into data science and machine learning roles.
Working data analysts who want to upgrade from basic analytics to machine learning and AI applications.
Career changers from non-technical backgrounds who are motivated to learn programming and data-driven problem solving
Entrepreneurs and innovators who want to apply AI techniques to real-world challenges and startup ideas.
Anyone passionate about hands-on learning, real-world case studies, and building job-ready skills in a fast-growing field.