Types of Analytics

Types of Analytics

• Descriptive Analytics

  • Make use of data aggregation and data mining to provide insight into the past and answer: “What has happened?”

• Predictive Analytics

  • Make use of statistical models and forecasts techniques to understand the future and answer: “What could or will happen?”

• Prescriptive Analytics

  • Make use of optimisation and simulation algorithms to advice on possible outcomes and answer: “What should we do?” or "How can we make it happen?"

Types of Analytics

Diagnostic Analytics

Example

Historical Data

Launch Day

What is Machine Learning?

What is Machine Learning?

[Machine Learning is the] field of study that gives computers the ability to learn without being explicitly programmed.

Arthur Samuel, 1959

Why Machine Learning?

Why Machine Learning

• The problem of a rule-based system against an evolving world. E.g., Spam filtering

  • Set rules versus ML

• ML is great for:

  • Problems the requires a lot of hand-tuning or long list of rules

  • Complex problems

  • Evolving environment: Adapts to new data

  • Pattern discovery / Provides insights

Types of Machine Learning Algorithms

Types of Machine Learning Algorithms

• Supervised

  • Regression

    • Linear Regression, Penalised Methods, Tree-based

  • Classification

    • Logistic Regression, Tree-based, Bagging, Boosting

• Unsupervised

  • Clustering

  • Visualisation / Dimensionality Reduction

  • Association rule learning

• Semi-supervised

• Reinforcement Learning

Deep Learning Use Case

Source: deeplearning4j

Regression -

Continuous Variable

Classification

Classification with Deep Learning

Unsupervised

Reinforcement Learning - 2015

Reinforcement Learning - 2017

Teachable Machine

Webcam Controller

Machine Learning Checklist

Machine Learning Checklist

1. Frame the problem and look at the big picture.

2. Get the data.

3. Explore the data to gain insights.

4. Prepare the data to better expose the underlying data patterns to Machine Learning algorithms.

5. Explore many different models and short-list the best ones.

6. Fine-tune your models and combine them into a great solution.

7. Present your solution.

8. Launch, monitor, and maintain your system.

Source: Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems By Aurélien Géron

References

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Source: https://livebook.manning.com/#!/book/deep-learning-with-python/about-this-book/

Source: http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram

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Source: https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/

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Source: https://www.anaconda.com/

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Source: https://www.quantopian.com/

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Source: http://scikit-learn.org

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Source: www.kaggle.com

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Source: https://colab.research.google.com/notebooks/welcome.ipynb

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Source: https://js.tensorflow.org/

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Source: https://playground.tensorflow.org/

SKILLS FRAMEWORK FOR ICT

SKILLS FRAMEWORK FOR ICT