Masterclass in AI and Machine Learning #254002

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"Masterclass in AI and Machine Learning" is an intensive 5-day program designed to provide participants with a comprehensive understanding of core AI and machine learning concepts, techniques, and applications. This course will equip participants with the knowledge and skills to leverage AI to solve real-world problems and drive innovation within their organizations.

Upon successful completion of this course, participants will be able to:
• Understand the fundamental concepts of AI and machine learning: Including supervised, unsupervised, and reinforcement learning.
• Implement and evaluate key machine learning algorithms: Such as regression, classification, clustering, and deep learning models.
• Work with real-world datasets: Prepare, clean, and analyze data for machine learning models.
• Utilize popular machine learning libraries and tools: Such as Python, TensorFlow, and PyTorch.
• Develop and deploy basic machine learning models: To solve real-world problems.
• Understand the ethical and societal implications of AI and machine learning.

This course is designed for:
• Data Scientists and Machine Learning Engineers: Seeking to enhance their skills and knowledge.
• Software Engineers: Interested in incorporating AI into their projects.
• Data Analysts: Looking to expand their skillset into machine learning.
• Researchers and Academics: Working in fields related to AI and data science.
• Individuals with a strong interest in AI and a desire to build a career in this field.

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

• Introduction to AI: Defining AI, machine learning, and deep learning.
• Types of AI: Supervised, unsupervised, and reinforcement learning.
• History and Evolution of AI: Key milestones and breakthroughs.
• AI Applications in the Real World: Examples of AI in various domains (e.g., healthcare, finance, self-driving cars).

• Supervised Learning:
o Regression, classification algorithms (linear regression, logistic regression, decision trees, support vector machines).
o Model evaluation and selection (metrics, cross-validation).
• Unsupervised Learning:
o Clustering algorithms (k-means, hierarchical clustering).
o Dimensionality reduction techniques (PCA).
• Hands-on Exercise:
o Implementing simple machine learning models using Python and a library like scikit-learn.

• Introduction to Deep Learning:
o Neural networks, deep neural networks, and their architectures.
o Convolutional Neural Networks (CNNs) for image recognition.
o Recurrent Neural Networks (RNNs) for natural language processing.

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• Deep Learning Frameworks:
o Introduction to TensorFlow and PyTorch.
o Hands-on exercise: Building and training a simple neural network.

• Natural Language Processing (NLP):
o Text classification, sentiment analysis, and machine translation.
o Introduction to NLP libraries (e.g., NLTK, spaCy).
• Computer Vision:
o Image classification, object detection, and image segmentation.
o Applications of computer vision in various domains.
• Reinforcement Learning:
o Introduction to reinforcement learning concepts.
o Applications of reinforcement learning (e.g., game playing, robotics).

• Ethical Considerations in AI:
o Bias in AI algorithms, fairness, and accountability.
o Privacy and security concerns.
o The societal impact of AI.
• The Future of AI:
o Emerging trends and technologies in AI.
o The role of AI in various industries.
o Career paths in AI and machine learning.

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