Artificial Intelligence (AI) and Machine Learning

Artificial Intelligence and Machine Learning is a branch of study or discipline which includes theories, standards, methods and innovations of various different domains like mathematics, cognitive science, electronics and embedded systems to make intelligent systems that mimic human behaviour

Beginner 0(0 Ratings) 0 Students enrolled English
Created by Multiversity Admin
Last updated Sun, 08-Oct-2023
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Course overview
Artificial Intelligence (AI) and Machine Learning have revolutionized the technology landscape, enabling machines to learn and make decisions, opening up endless possibilities across various industries. This comprehensive 24-week AI Boot Camp is designed to equip participants with the knowledge and skills necessary to understand, apply, and excel in the fields of AI and machine learning.

What will i learn?

  • Preparing for interviews and industry certifications
  • Explore practical applications of AI and machine learning in real-world scenarios.
  • Build and train machine learning models.
  • Understand the ethical considerations and societal implications of AI.
  • Gain hands-on experience through projects and assignments.
  • Prepare for a career in AI or further academic pursuits in the field.
Curriculum for this course
33 Lessons 00:00:00 Hours
Week 1-2: Introduction to AI and Machine Learning
3 Lessons 00:00:00 Hours
  • Overview of AI and ML
    .
  • History and evolution of AI
    .
  • Types of machine learning: supervised, unsupervised, and reinforcement learning
    .
Week 3-4: Python Programming for AI
2 Lessons 00:00:00 Hours
  • Python fundamentals for data manipulation
    .
  • Libraries for AI and ML: NumPy, Pandas, Matplotlib, and Scikit-Learn
    .
Week 5-6: Data Preprocessing and Exploration
3 Lessons 00:00:00 Hours
  • Data collection and cleaning
    .
  • Feature selection and engineering
    .
  • Data visualization for insights
    .
Week 7-8: Supervised Learning
3 Lessons 00:00:00 Hours
  • Linear regression
    .
  • Logistic regression
    .
  • Decision trees and random forests
    .
Week 9-10: Unsupervised Learning
2 Lessons 00:00:00 Hours
  • Clustering algorithms: K-Means, Hierarchical, DBSCAN
    .
  • Dimensionality reduction: PCA and t-SNE
    .
Week 11-12: Neural Networks and Deep Learning
3 Lessons 00:00:00 Hours
  • Introduction to neural networks
    .
  • Deep learning frameworks: TensorFlow and PyTorch
    .
  • Building and training deep neural networks
    .
Week 13-14: Natural Language Processing (NLP)
3 Lessons 00:00:00 Hours
  • Text preprocessing
    .
  • Sentiment analysis
    .
  • Sequence models: RNNs and LSTMs
    .
Week 15-16: Computer Vision
3 Lessons 00:00:00 Hours
  • Image preprocessing
    .
  • Convolutional Neural Networks (CNNs)
    .
  • Object detection and image classification
    .
Week 17-18: Reinforcement Learning
3 Lessons 00:00:00 Hours
  • Markov Decision Processes (MDPs)
    .
  • Q-learning and policy gradients
    .
  • Applications in gaming and robotics
    .
Week 19-20: Ethics and Bias in AI
3 Lessons 00:00:00 Hours
  • AI ethics and responsible AI development
    .
  • Bias in AI algorithms
    .
  • Fairness and transparency in AI
    .
Week 21-22: AI Projects and Case Studies
2 Lessons 00:00:00 Hours
  • Capstone project development
    .
  • Real-world AI applications and success stories
    .
Week 23-24: Future of AI and Career Development
3 Lessons 00:00:00 Hours
  • Emerging trends in AI
    .
  • Career paths in AI and machine learning
    .
  • Preparing for interviews and industry certifications
    .
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Multiversity Admin

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