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Introduction to Machine Learning
Introduction to Machine Learning
What is Machine Learning
• Machine learning is an application of
artificial intelligence that involves
algorithms and data that automatically
analyse and make decision by itself
without human intervention.
• It describes how computer perform tasks
on their own by previous experiences.
• Therefore we can say in machine
language artificial intelligence is generated
on the basis of experience.
Normal Computer vs ML
• The difference between normal computer
software and machine learning is that a
human developer hasn’t given codes that
instructs the system how to react to
situation, instead it is being trained by a
large number of data.
Some of the machine learning algorithms are:
• Neural Networks
• Random Forests
• Decision trees
• Genetic algorithm
• Radial basis function
• Sigmoid
Types of Machine Learning
There are three types of machine learning
– Supervised learning
– Unsupervised learning
– Reinforcement learning
Machine Learning Uses:
• Traffic prediction
• Virtual Personal Assistant
• Speech recognition
• Email spam and malware filtering
• Bioinformatics
• Natural language processing
Real Time Examples for ML
• TRAFFIC PREDICTION
• VIRTUAL PERSONAL ASSISTANT
• ONLINE TRANSPORTATION
• SOCIAL MEDIA SERVICES
• EMAIL SPAM FILTERING
• PRODUCT RECOMMENDATION
• ONLINE FRAUD DETECTION
Best Programming Languages for ML
Some of the best and most commonly used machine learning programs
are
• Python,
• java,
• C,
• C++,
• Shell,
• R,
• JavaScript,
• Scala,
• Shell,
• Julia
Difference Between Machine Learning
And Artificial Intelligence
• Artificial Intelligence is a concept of
creating intelligent machines that
stimulates human behaviour whereas
Machine learning is a subset of Artificial
intelligence that allows machine to learn
from data without being programmed.
Advantages of Machine Learning
• Fast, Accurate, Efficient.
• Automation of most applications.
• Wide range of real life applications.
• Enhanced cyber security and spam
detection.
• No human Intervention is needed.
• Handling multi dimensional data.
Disadvantages of Machine Learning
• It is very difficult to identify and rectify the
errors.
• Data Acquisition.
• Interpretation of results Requires more
time and space.
Thank you

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Intro to ML: Learn What ML Is, Types, Uses, Examples & More

  • 3. What is Machine Learning • Machine learning is an application of artificial intelligence that involves algorithms and data that automatically analyse and make decision by itself without human intervention. • It describes how computer perform tasks on their own by previous experiences. • Therefore we can say in machine language artificial intelligence is generated on the basis of experience.
  • 4. Normal Computer vs ML • The difference between normal computer software and machine learning is that a human developer hasn’t given codes that instructs the system how to react to situation, instead it is being trained by a large number of data.
  • 5. Some of the machine learning algorithms are: • Neural Networks • Random Forests • Decision trees • Genetic algorithm • Radial basis function • Sigmoid
  • 6. Types of Machine Learning There are three types of machine learning – Supervised learning – Unsupervised learning – Reinforcement learning
  • 7. Machine Learning Uses: • Traffic prediction • Virtual Personal Assistant • Speech recognition • Email spam and malware filtering • Bioinformatics • Natural language processing
  • 8. Real Time Examples for ML • TRAFFIC PREDICTION • VIRTUAL PERSONAL ASSISTANT • ONLINE TRANSPORTATION • SOCIAL MEDIA SERVICES • EMAIL SPAM FILTERING • PRODUCT RECOMMENDATION • ONLINE FRAUD DETECTION
  • 9. Best Programming Languages for ML Some of the best and most commonly used machine learning programs are • Python, • java, • C, • C++, • Shell, • R, • JavaScript, • Scala, • Shell, • Julia
  • 10.
  • 11. Difference Between Machine Learning And Artificial Intelligence • Artificial Intelligence is a concept of creating intelligent machines that stimulates human behaviour whereas Machine learning is a subset of Artificial intelligence that allows machine to learn from data without being programmed.
  • 12. Advantages of Machine Learning • Fast, Accurate, Efficient. • Automation of most applications. • Wide range of real life applications. • Enhanced cyber security and spam detection. • No human Intervention is needed. • Handling multi dimensional data.
  • 13. Disadvantages of Machine Learning • It is very difficult to identify and rectify the errors. • Data Acquisition. • Interpretation of results Requires more time and space.
  • 14.