- Introduction to AI and Data Science
- Python for Data Science — Setup and Basics
- Data Types, Variables, and Structures
- Data Collection and Web Scraping
- Data Cleaning and Preprocessing
- Exploratory Data Analysis (EDA)
- Data Visualization with Matplotlib and Seaborn
- Statistics and Probability for Data Science
- Linear Algebra and Math Essentials
- Introduction to Machine Learning
- Supervised Learning Algorithms
- Unsupervised Learning Algorithms
- Model Evaluation and Validation
- Deep Learning with Neural Networks
- Natural Language Processing (NLP)
- Computer Vision Basics
- Working with Big Data Tools
- AI Ethics and Responsible AI
- Deploying AI Models
- Capstone Project — AI & Data Science Application
AI & Data Science: From Fundamentals to Advanced Applications will equip you with the skills to turn raw data into actionable insights and intelligent solutions. Through practical exercises, projects, and guided instruction, you’ll learn how to work with datasets, build machine learning models, and implement AI-powered applications.
What you’ll learn:
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Python programming for data science
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Data cleaning, preprocessing, and visualization
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Statistics and probability for data analysis
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Supervised and unsupervised machine learning techniques
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Deep learning fundamentals with neural networks
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Working with popular libraries like Pandas, NumPy, Matplotlib, and Scikit-learn
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Introduction to AI concepts such as natural language processing and computer vision
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Deploying models into real-world applications
Inside the course:
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10 modules, 50+ lessons, 6 major projects
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Interactive coding challenges and datasets provided
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Downloadable notebooks and resources
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Community discussion board and optional mentor support
Who should enroll:
Beginners wanting to enter AI and data science, analysts seeking advanced skills, and developers aiming to integrate machine learning into their work.
Prerequisites:
Basic knowledge of Python is recommended but not required. No prior AI or data science experience necessary.
Curriculum overview:
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Introduction to AI & Data Science
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Python for Data Analysis
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Data Cleaning, Preprocessing, and Visualization
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Statistics and Probability
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Supervised Machine Learning
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Unsupervised Machine Learning
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Deep Learning Fundamentals
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Natural Language Processing Basics
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Computer Vision Essentials
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Model Deployment and Final Project
Projects you’ll build:
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Data Analysis Dashboard
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Predictive Sales Model
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Customer Segmentation Tool
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Sentiment Analysis Application
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Image Classification Model
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End-to-End AI-Powered Project
Schedule & delivery:
Self-paced online learning with lifetime access to materials.
Certification:
Earn a professional, shareable digital certificate upon completing all lessons and projects.
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