Creating tomorrow: Machine Learning

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CREATING TOMORROW
THE POWER OF MACHINE LEARNING
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Contents

• Introduction
• Automation Algorithms
• Types
• Methodology
• Technical Architecture
• Results and Achievements
• Conclusion

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Introduction
Machine learning is a field of artificial
intelligence where computers learn from data and
patterns, improving their performance on tasks
without explicit programming. It's used in
various applications, from recommendation systems
to image recognition and medical diagnosis. Key
techniques include supervised learning,
unsupervised learning, and reinforcement
learning.Feel free to adapt and expand upon this
introduction to suit your specific presentation
or document needs.
Machine Learning
Machine learning online Training is a subset of
artificial intelligence that enables computers to
learn from data and make predictions or decisions
without explicit programming.
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MACHINE LEARNING
Automation in ML simplifies data processing,
model selection, tuning, deployment, and
maintenance, streamlining the ML pipeline to
enhance efficiency and accessibility for
non-experts.
In machine learning, an algorithm is a
step-by-step computational procedure that enables
computers to learn from data and make predictions
or decisions without explicit programming.
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TYPES
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Unsupervised Learning
Reinforcement Learning
Semi-Supervised Learning
Supervised Learning
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PROBLEM FORMULATION
INCREASED EFFICIENCY
DATA SPLITTING
ENHANCED ACCURACY
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TECHNICAL ARCHITECTURE
Technical architecture in machine learning refers
to the design and structure of the computational
and software infrastructure that supports the
development, deployment, and execution of machine
learning models and applications. It encompasses
a set of components and technologies that work
together to enable the entire machine learning
workflow
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RESULTS AND ACHIEVEMENTS
Deep learning, a subset of machine learning that
utilizes deep neural networks, has led to
remarkable advancements in various applications.
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Reinforcement learning, a machine learning
paradigm where agents learn to make decisions to
maximize a reward signal, has achieved notable
results, especially in challenging domains.
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THANK YOU!
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