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Introduction to Artificial Intelligence

  • Overview of AI and its History
    Types of AI: Narrow AI vs. General AI vs. Superintelligence
    AI in the Modern World and Industry Applications
    Ethical Issues in AI and its Impact on Society

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2. Fundamentals of Machine Learning

Introduction to Machine Learning (ML)
Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
Key Concepts: Training, Testing, Overfitting, Bias-Variance Tradeoff
Common Algorithms: Linear Regression, Decision Trees, K-Nearest Neighbors (KNN), etc.

 

3. Data Preprocessing and Feature Engineering

Data Collection and Preparation

Data Cleaning: Handling Missing Data and Outliers

Feature Engineering and Selection

Scaling, Normalization, and Encoding Categorical Data

 

4. Supervised Learning

Overview of Supervised Learning
Regression Algorithms: Linear and Polynomial Regression
Classification Algorithms: Logistic Regression, Support Vector Machines (SVM), k-NN, Decision Trees, Random Forests Evaluation Metrics: Accuracy, Precision, Recall, F1-Score, ROC Curve

  • 5. Unsupervised Learning
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  • Overview of Unsupervised Learning
    Clustering Algorithms: K-Means, DBSCAN, Hierarchical Clustering
    Dimensionality Reduction: Principal Component Analysis (PCA), t-SNE
    Anomaly Detection and Applications

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  • 6. Neural Networks and Deep Learning
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  • Introduction to NLP and Text Preprocessing
    Text Representation: Bag of Words, TF-IDF, Word Embeddings (Word2Vec, GloVe)
    Sentiment Analysis and Text Classification
    Named Entity Recognition (NER), Part-of-Speech Tagging
    Advanced NLP Models: Transformer, BERT, GPT

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  • 8. Reinforcement Learning

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  • Introduction to Reinforcement Learning (RL)
    Key Concepts: Agent, Environment, Reward, Policy
    Q-Learning and Deep Q Networks (DQN)
    Markov Decision Processes (MDP)
    Applications of RL (Robotics, Games, Autonomous Vehicles)
  • 9. Computer Vision
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Overview of Computer Vision
Image Preprocessing and Augmentation
Object Detection and Recognition
Convolutional Neural Networks (CNNs) in Vision
Transfer Learning and Pre-trained Models (e.g., VGG, ResNet, YOLO)

10. AI Ethics and Safety
 

  • Ethical Considerations in AI Development
    Bias and Fairness in AI Models
    AI in Decision-Making and Accountability
    AI Regulation and Privacy Concerns (GDPR, Data Privacy)
    AI Safety and Control

 

  • 11. AI Tools and Frameworks
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  • Introduction to Popular AI Tools and Libraries
    Machine Learning Libraries: Scikit-learn, XGBoost, LightGBM
    Deep Learning Frameworks: TensorFlow, PyTorch, Keras
    Cloud-Based AI Services: Google AI, Azure AI, AWS SageMaker

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  • 12. Model Deployment and Optimization
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  • Introduction to Model Deployment
    Deploying AI Models in Production
    Model Optimization: Hyperparameter Tuning, Grid Search, Random Search
    Monitoring and Maintaining AI Systems in Production

    • 13. Advanced Topics in AI
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    • Generative Adversarial Networks (GANs)
      Transfer Learning and Few-Shot Learning
      Meta-Learning and Self-Supervised Learning
      Explainable AI (XAI)

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    • 14. AI in Industry Applications
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    • AI in Healthcare: Diagnostics, Personalized Medicine
      AI in Finance: Fraud Detection, Algorithmic Trading
      AI in Autonomous Vehicles
      AI in Robotics and Automation
      AI for Predictive Analytics and Business Intelligence

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    • 15. Case Studies and Projects
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  • Real-World AI Case Studies and Industry Applications
    Hands-on AI Projects (Image Classification, Chatbots, Predictive Models)
    End-to-End AI Project: Data Collection, Model Building, Evaluation, and Deployment