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Applied Data Science and AI/Machine Learning for Cybersecurity

A common misconception is that predictive analytics and machine learning (ML) are the same thing.

At its core, predictive analytics encompasses a variety of statistical techniques (including machine learning, predictive modelling and data mining) and uses statistics (both historical and current) to estimate, or predict, future outcomes.

These outcomes might be behaviors a customer is likely to exhibit or possible changes in the market, for example. Predictive analytics help us to understand possible future occurrences by analyzing the past

Predictive analytics is driven by predictive modelling. It’s more of an approach than a process. Predictive analytics and machine learning go hand in hand, as predictive models typically include a machine learning algorithm.

These models can be trained over time to respond to new data or values, delivering the results the business needs. Predictive modelling largely overlaps with the field of machine learning.

Some of the popular machine learning models for predictive analytics include:

  1. Linear regression – This model is used to predict a continuous dependent variable based on one or more independent variables.
  2. Decision trees – This model uses a tree-like structure to represent decisions and their possible consequences.
  3. Random Forest – This ensemble model combines multiple decision trees to improve accuracy and avoid overfitting. Overfitting in predictive analytics occurs when a model is trained too closely on its training data, resulting in a model that is unable to generalize well to new data. This can lead to inaccurate predictions and poor performance on unseen data.
  4. Support Vector Machines (SVM) – This model is used to classify data into two or more categories by finding the best separating hyperplane. A separating hyperplane is a linear decision boundary that separates two classes of data points. It is a useful tool for classification tasks, such as predicting whether a customer will churn, whether a loan applicant is likely to default, or whether a medical image contains a tumor.
  5.  Neural networks – These models are inspired by the structure and function of the human brain and can be used for both classification and regression.

Want to learn more? Tonex offers Predictive Analytics and Machine Learning Models, a 2-day course where participants learn the core concepts of predictive analytics and machine learning.

Participants will also gain proficiency in data preprocessing, feature engineering, and data visualization as well as learning to master various machine learning algorithms and their applications.

This course is ideal for:

  • Data Scientists and Analysts seeking to enhance their predictive modeling skills.
  • Business Analysts and Managers interested in leveraging data for decision-making.
  • IT professionals looking to transition into data science and machine learning roles.
  • Researchers and academics exploring the applications of machine learning.
  • Entrepreneurs and business leaders interested in data-driven strategies.

For more information, questions, comments, contact us.

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