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Machine Learning Algorithms and Analytics

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Profile

  • Lecturer at the Cross River State College of Health Technology, Calabar.

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  • PhD in Computer Science (Data Science) – University of Calabar.

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  • Master of Information Technology (Software Engineering) – University of Sydney, Australia.

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  • Research interests include Internet of Things (IoT), Artificial Neural Networks and Software Development.

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  • Recipient of National Mathematics Center Scholarship, Chevron/NNPC Joint Venture Scholarship and Australia Awards Scholarship.

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Presentation Outline

  • Definition of Algorithm
  • Overview of Machine Learning
  • Machine Learning Types
  • Machine Learning Problem Domains
  • Machine Learning Algorithms

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Expected Outcomes

  1. Understand the meaning of algorithm
  2. Have basic knowledge of the Machine Learning paradigm and its types.
  3. Understand how to solve problems using ML
  4. Have knowledge of ML algorithms and how to apply them

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What is an Algorithm?

An algorithm describes the steps or procedures taken in solving a given task or performing a computation.

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Example: Algorithm ATM cash withdrawal

Step 1: Insert ATM Card: ...

Step 2: Select Language. ...

Step 3: Enter 4-Digit ATM Pin: ...

Step 4: Select the type of Transaction: ...

Step 5: Select the Type of Account: ...

Step 6: Enter the withdrawal amount. ...

Step 7: Verify balance: ...

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Step n: Take a printed receipt , if needed

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Overview of Machine Learning

“At its core, machine learning is a subset of artificial intelligence (AI) that enables computers to learn from data without being explicitly programmed. Think of it as teaching a computer to recognize patterns and make decisions based on examples rather than explicit instructions.”

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(Source: https://www.linkedin.com/pulse/what-machine-learning-laymans-guide-understanding-basics-chowdhury-qor6c)

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Overview of Machine Learning

Some real world applications of ML include:

  1. Self-driving cars
  2. Facial recognition
  3. Product recommendations
  4. Email automation and spam filtering
  5. Financial accuracy
  6. Social media optimization
  7. Healthcare advancement
  8. Mobile voice to text and predictive text
  9. Predictive analytics

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Machine Learning Types

  1. Supervised learning
  2. Unsupervised learning
  3. Semi-supervised learning
  4. Reinforcement learning

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Machine Learning Types

Supervised learning

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Machine Learning Types

Unsupervised learning

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Machine Learning Types

Semi-supervised learning

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Machine Learning Types

Reinforcement learning

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Machine Learning Problem Domains

  • Classification (Binary or Multiclass)
  • Anomaly (outlier) Detection
  • Regression
  • Clustering
  • Reinforcement Learning

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Machine Learning Algorithms

  1. Linear Regression
  2. Decision Trees
  3. Random Forest
  4. Support Vector Machines
  5. K-means Clustering
  6. Principal Component Analysis
  7. Apriori Algorithm

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Machine Learning Algorithms

Linear Regression

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(Source: https://www.datacamp.com/blog/top-machine-learning-use-cases-and-algorithms)

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Machine Learning Algorithms

Decision Tree

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(Source: https://www.datacamp.com/blog/top-machine-learning-use-cases-and-algorithms)

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Machine Learning Algorithms

Random Forest

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(Source: https://www.datacamp.com/blog/top-machine-learning-use-cases-and-algorithms)

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Machine Learning Algorithms

Support Vector Machine

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(Source: https://www.datacamp.com/blog/top-machine-learning-use-cases-and-algorithms)

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Machine Learning Algorithms

K-Means Clustering

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(Source: https://www.datacamp.com/blog/top-machine-learning-use-cases-and-algorithms)

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Machine Learning Algorithms

Principal Component Analysis

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(Source: https://www.datacamp.com/blog/top-machine-learning-use-cases-and-algorithms)

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Machine Learning Algorithms

Apriori Algorithm

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(Source: https://www.datacamp.com/blog/top-machine-learning-use-cases-and-algorithms)

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Machine Learning Algorithms

  • Practice question to be sent later today.
  • All participates to complete the task by 10:00am tomorrow.
  • Completed tasks to be forward to isaacrajuno@gmail.com
  • Submitted tasks will be assessed and results forwarded to participants.

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Online Resources for further reading

https://www.datacamp.com/blog/top-machine-learning-use-cases-and-algorithms

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https://en.wikipedia.org/wiki/Machine_learning

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https://www.researchgate.net/profile/Batta-Mahesh/publication/344717762_Machine_Learning_Algorithms_-A_Review/links/5f8b2365299bf1b53e2d243a/Machine-Learning-Algorithms-A-Review.pdf?eid=5082902844932096t

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https://link.springer.com/article/10.1007/S42979-021-00592-X

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https://link.springer.com/chapter/10.1007/978-3-319-18305-3_1

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THANK

YOU