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  • AI Developer Advanced Course-Hands-On, Project-Based Learning-(Track 2)

    AI Developer Advanced Course-Hands-On, Project-Based Learning-(Track 2)

    CDsuper CDsuper

    Number of lessons: 20

  • AI Developer Fundamentals – Prerequisite Course (Track 1)

    AI Developer Fundamentals – Prerequisite Course (Track 1)

    CDsuper CDsuper

    Number of lessons: 27

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Course Syllabus

  • Chapter 1 – Foundations of AI & Machine Learning
    • Lesson 1.1 – Introduction to AI & Machine Learning
    • Lesson 1.2 – Key AI Career Paths & Industry Demand
    • Lesson 1.3 – History & Evolution of AI
    • Lesson 1.4 – Understanding Supervised vs. Unsupervised Learning
    • Lesson 1.5 – Intro to Neural Networks & AI Model Training Basics
  • Chapter 2 – Python for AI Development
    • Lesson 2.1 – Getting Started with Python (Syntax, Variables, Data Types)
    • Lesson 2.2 – Python Control Flow: Loops & Conditionals
    • Lesson 2.3 – Functions, Modularization & Object-Oriented Programming (OOP)
    • Lesson 2.4 – Working with Lists, Dictionaries & Tuples in AI Development
    • Lesson 2.5 – Debugging & Error Handling in Python
    • Lesson 2.6 – Hands-On Project: AI-Powered Calculator for Statistical Analysis
  • Chapter 3 – Mathematics for AI
    • Lesson 3.1 – Linear Algebra for AI (Vectors, Matrices, Eigenvalues)
    • Lesson 3.2 – Probability & Statistics (Distributions, Bayes Theorem, Hypothesis Testing)
    • Lesson 3.3 – Calculus in AI (Gradients, Derivatives, Optimization Techniques)
    • Lesson 3.4 – Understanding Cost Functions & Loss in AI Models
    • Lesson 3.5 – Hands-On Project: Building a Simple Neural Network from Scratch
  • Chapter 4 – Data Science & Machine Learning Fundamentals
    • Lesson 4.1 – Data Preprocessing & Cleaning Techniques
    • Lesson 4.2 – Feature Engineering: Transforming Raw Data for ML Models
    • Lesson 4.3 – Supervised Learning: Regression & Classification Models
    • Lesson 4.4 – Unsupervised Learning: Clustering & Dimensionality Reduction
    • Lesson 4.5 – Model Evaluation: Accuracy, Precision, Recall & F1 Score
    • Lesson 4.6 – Hands-On Project: Predictive Model for Customer Behavior
  • Chapter 5 – AI Ethics & Responsible AI Development
    • Lesson 5.1 – Bias & Fairness in AI Models
    • Lesson 5.2 – Privacy & Security Concerns in AI Applications
    • Lesson 5.3 – Regulatory Guidelines & Ethical AI Decision-Making
    • Lesson 5.4 – Explainability & Transparency in AI Systems
    • Lesson 5.5 – Hands-On Project: Detecting Bias in Datasets Using Python

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