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25 hrs

Learning content

Python Programming in Artificial Intelligence
Introduction to Artificial Intelligence
Quiz Icon 3 Quizzes
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    Quiz Icon 3 Quizzes
    Project Icon 1 Project
    • What is Artificial Intelligence?
       
    • AI History and Milestones
       
    • Types of AI: Narrow vs. General AI
       
    • Review of Python Basics
       
    • NumPy and SciPy for Scientific Computing
       
    • Pandas for Data Manipulation
       
Machine Learning Fundamentals
Quiz Icon 4 Quizzes
Project Icon 1 Project
    Quiz Icon 4 Quizzes
    Project Icon 1 Project
    • Machine Learning Introduction
       
    • Types of Machine Learning: Supervised, Unsupervised, Reinforcement
       
    • The Machine Learning Workflow
       
    • Scikit-Learn Overview
       
    • Data Preprocessing and Cleaning
       
    • Handling Missing Data
       
    • Exploratory Data Analysis (EDA)
       
    • Matplotlib and Seaborn for Data Visualization
       
Supervised Learning
Quiz Icon 2 Quizzes
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    Quiz Icon 2 Quizzes
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    • Linear Regression
       
    • Simple Linear Regression
       
    • Multiple Linear Regression
       
    • Classification Algorithms
       
    • Logistic Regression
       
    • K-Nearest Neighbors (KNN)
       
    • Decision Trees and Random Forest
       
Unsupervised Learning
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    Quiz Icon 3 Quizzes
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    • Clustering Algorithms
       
    • K-Means Clustering
       
    • Hierarchical Clustering
       
    • Dimensionality Reduction
       
    • Principal Component Analysis (PCA)
       
    • t-Distributed Stochastic Neighbor Embedding (t-SNE)
       
Neural Networks and Deep Learning
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    Quiz Icon 3 Quizzes
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    • Introduction to Neural Networks
       
    • Perceptrons and Activation Functions
       
    • Building Neural Networks with TensorFlow/Keras
       
    • Deep Learning
       
    • Convolutional Neural Networks (CNNs)
       
    • Recurrent Neural Networks (RNNs)
       
    • Transfer Learning
       
Advanced AI Topics
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    • Natural Language Processing (NLP)
       
    • Tokenization and Text Preprocessing
       
    • Building NLP Models with Python (e.g., SpaCy, NLTK)
       
    • Reinforcement Learning and AI Ethics
       
    • Markov Decision Processes (MDPs)
       
    • Q-Learning and Deep Q-Networks (DQNs)
       
    • Ethical Considerations in AI
       
Final Project and Course Review
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    • Course Review and Exam: A final review of key concepts and an assessment to test students' knowledge.
Additional Topics (Optional, depending on course duration)
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    • Time Series Analysis
       
    • Generative Adversarial Networks (GANs)
       
    • AI Deployment and Model Serving
       
    • Explainable AI (XAI)
       
    • AI in Computer Vision
       

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