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Artificial Intelligence And Machine Learning

This course offers an in-depth overview of Artificial Intelligence (AI) and Machine Learning (ML), emphasizing essential principles, tools, and their practical applications.

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Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

  1. This course offers an in-depth overview of Artificial Intelligence (AI) and Machine Learning (ML), emphasizing essential principles, tools, and their practical applications.
  2. It explores important methodologies including supervised and unsupervised learning, neural networks, deep learning, natural language processing, and reinforcement learning.
  3. Participants will engage in practical exercises using widely-used programming frameworks such as Python, TensorFlow, and PyTorch. Upon completion of the course, learners will be prepared to construct, assess, and implement AI and ML models to address real-world challenges.

CERTIFICATION:

  1. Certificate of Completion (provided by the institution that conducted the course).
  2. Industry Certifications (if they correspond with external examinations such as Google TensorFlow Developer, Microsoft AI Fundamentals, or AWS Machine Learning Specialty).
  3. Professional Development Units (PDUs) for continuing education credits, if relevant.
  4. Digital Badges (for example, on platforms like Coursera, Udemy, or LinkedIn Learning).

LEARNING OUTCOMES:

By the conclusion of the course, participants will possess the skills to:

  1. Comprehend AI and ML Fundamentals: Acquire knowledge of the theoretical and mathematical concepts that form the basis of AI and ML algorithms.
  2. Create and Train Models: Develop machine learning models utilizing supervised, unsupervised, and reinforcement learning methodologies.
  3. Conduct Data Analysis: Engage in data preprocessing, visualization, and exploratory analysis to uncover trends and insights.
  4. Construct Neural Networks: Learn to design and implement deep learning architectures, including convolutional and recurrent neural networks.
  5. Implement AI Solutions: Address real-world challenges in fields such as computer vision, natural language processing, and recommendation systems.
  6. Assess Model Effectiveness: Employ metrics like accuracy, precision, recall, and F1-score to evaluate and refine models.
  7. Explore Ethics and Implications of AI: Gain an understanding of the ethical considerations, limitations, and societal effects of AI technologies.
  8. Deploy AI Applications: Master the deployment of machine learning models in production settings using cloud services and APIs.
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Course Curriculum

Fundamentals
  1. Introduction to AI and ML
  2. History, types (supervised, unsupervised, reinforcement learning)
  3. Applications of AI/ML in various industries
  4. Ethics and limitations of AI
Mathematics and Statistics for AI/ML
  1. Linear algebra: Matrices, vectors, and transformations
  2. Probability and statistics: Distributions, Bayes theorem
  3. Calculus: Derivatives, optimization, and gradient descent
Programming and Tools
  1. Introduction to Python for AI/ML
  2. Libraries: NumPy, pandas, Matplotlib, and scikit-learn
  3. Frameworks: TensorFlow, PyTorch
Machine Learning Algorithms
  1. Regression (Linear, Logistic)
  2. Classification (SVM, Decision Trees, Random Forests, K-Nearest Neighbors)
  3. Clustering (K-Means, DBSCAN, Hierarchical Clustering)
  4. Ensemble Learning
Deep Learning
  1. Neural Networks: Basics, activation functions, backpropagation
  2. Convolutional Neural Networks (CNNs): Image processing
  3. Recurrent Neural Networks (RNNs): Sequential data processing
  4. Generative Adversarial Networks (GANs)
Advanced Topics
  1. Natural Language Processing (NLP): Tokenization, transformers
  2. Computer Vision: Object detection, segmentation
  3. Reinforcement Learning
  4. Transfer Learning
AI Deployment
  1. Model evaluation and tuning
  2. Deployment strategies
  3. Working with APIs and cloud services
  4. Monitoring and maintaining ML models
Capstone Project
  1. End-to-end AI/ML project with data preprocessing, model building, and deployment.

Training Features

Hands-on Projects

Focused on real-world applications like fraud detection, sentiment analysis, and recommendation systems.

Interactive Learning

Use of virtual labs, coding exercises, and live coding sessions.

Industry-Relevant Tools

Practical training on popular frameworks like TensorFlow, PyTorch, and scikit-learn.

Personalized Feedback

Regular feedback on assignments and projects from mentors.

Career Support

Resume building, interview preparation, and guidance for AI/ML job roles.

Certification

A globally recognized certificate upon completion of the course.

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