Data Science & AI Mastery: From Basics to Deployment

Name: Data Science & AI Mastery: From Basics to Deployment
Availability: InStock
Rating: 4.40 (954 reviews)

4.40

9,540 students

3h 34m

Updated Apr 2026

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.

Course Description

“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.

Requirements

Basic computer literacy – comfort with installing software, navigating files, and using online tools.
Familiarity with mathematics fundamentals – especially algebra, probability, and basic statistics (no advanced math required).
Beginner-level Python knowledge – understanding variables, loops, functions, and simple data structures.
A computer with Windows, macOS, or Linux and internet connection to run tools and complete labs.
Curiosity, persistence, and a willingness to learn by doing—no prior machine learning or AI experience required.