Duration: 8-12 Weeks
Mode: Online
Level: Beginner to Intermediate
Prerequisites: Basic Python, Statistics, Algebra
Curriculum
- 9 Sections
- 33 Lessons
- 8 Weeks
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- Module 1: Introduction to Artificial Intelligence & Machine LearningThis module introduces the fundamentals of Artificial Intelligence (AI) and Machine Learning (ML). It covers the key concepts, types of ML, and real-world applications.6
- 1.1Definition and Scope of AI & ML
- 1.2Differences: AI vs. ML vs. Deep Learning vs. Data Science
- 1.3Types of ML: Supervised, Unsupervised, Reinforcement Learning
- 1.4Industry Applications: Healthcare, Finance, Retail, Autonomous Vehicles, etc.
- 1.5Real-World Example: Netflix Recommendation System
- 1.6Quiz #110 Minutes3 Questions
- Module 2: Python for AI & MLThis module provides a strong foundation in Python programming, focusing on essential libraries for AI & ML.4
- Module 3: Data Preprocessing & Feature EngineeringThis module covers techniques to clean and preprocess raw data to improve ML model accuracy.5
- Module 4: Supervised Learning – Regression & ClassificationThis module focuses on supervised learning techniques used for prediction and classification.4
- Module 5: Unsupervised Learning – Clustering & Dimensionality ReductionThis module covers methods to uncover hidden patterns in unlabeled data.4
- Module 6: Deep Learning & Neural NetworksThis module introduces deep learning concepts and neural networks, including CNNs for image processing.6
- Module 7: Natural Language Processing (NLP)This module focuses on processing and analyzing textual data.5
- Module 8: Model Deployment & AI EthicsThis module teaches how to deploy AI models and explores ethical considerations in AI.6
- Maximize Your Investment with MEH1
Instructor
