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Artificial Intelligence and Machine Learning Trainig

Description

Training: Artificial Intelligence and Machine Learning Indepth

Duration: 60 Hours

Table of Contents:

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Module 1: Introduction to Artificial Intelligence and Machine Learning (8 hours)

  • What is AI and ML?
  • History and Evolution of AI/ML
  • Types of AI and ML: Supervised learning, unsupervised learning, reinforcement learning
  • Applications of AI/ML across various industries
  • Ethical considerations and bias in AI/ML

Module 2: Statistics and Data Preprocessing (8 hours)

  • Essential statistics concepts: Descriptive statistics, hypothesis testing, correlation, etc.
  • Data preprocessing techniques: Data cleaning, normalization, feature scaling, etc.
  • Exploratory data analysis (EDA) using Python libraries like Pandas and NumPy
  • Introduction to data visualization and storytelling with libraries like Matplotlib and Seaborn

Module 3: Machine Learning Fundamentals (12 hours)

  • Supervised learning algorithms:
    • Linear Regression
    • Logistic Regression
    • Decision Trees and Random Forests
    • K-Nearest Neighbors (KNN)
    • Support Vector Machines (SVM)
  • Unsupervised learning algorithms:
    • K-Means Clustering
    • Principal Component Analysis (PCA)
  • Model evaluation metrics and performance analysis

Module 4: Deep Learning and Neural Networks (12 hours)

  • Introduction to artificial neural networks:
    • Artificial neurons and activation functions
    • Deep learning architectures: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs)
  • TensorFlow and PyTorch for Deep Learning:
    • Deep learning frameworks and their applications
    • Building and training deep learning models
    • Model optimization and hyperparameter tuning

Module 5: Natural Language Processing (NLP) (12 hours)

  • Fundamentals of NLP: Text processing, tokenization, stemming, lemmatization
  • Sentiment analysis and opinion mining
  • Text classification and topic modeling
  • Word embeddings and language models
  • NLP applications in chatbots, virtual assistants, and text summarization

Module 6: Computer Vision and Image Processing (8 hours)

  • Fundamentals of image processing: Image representation, noise reduction, segmentation
  • Feature extraction and object detection
  • Image classification and recognition with deep learning
  • Applications of computer vision in image recognition, medical imaging, and autonomous vehicles

Module 7: Hands-on Labs and Projects (10 hours)

  • Practical exercises on various ML algorithms
  • Building ML models for real-world problems
  • Participating in group projects and hackathons
  • Presenting and discussing project results

Module 8: Deploying and Monitoring ML Models (4 hours)

  • Model deployment options: Cloud platforms, on-premise servers, edge computing
  • Monitoring and evaluating model performance in production
  • Model maintenance and updates

Bonus Module: Ethics and Responsible AI (2 hours)

  • Algorithmic bias and fairness
  • Explainability and interpretability of AI models
  • Privacy concerns and data security
  • Responsible development and deployment of AI solutions

Additional Resources:

  • Online courses and tutorials
  • Books and articles on AI/ML
  • Open source libraries and datasets
  • Kaggle competitions and challenges
  • AI/ML conferences and workshops

Note:

  • This is a suggested TOC and may need to be adapted based on the specific needs and experience level of the corporate client.
  • Additional modules or topics can be included based on specific requirements.
  • Hands-on labs and projects are crucial for practical learning and should be incorporated throughout the training.

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