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Online Certified Course

Understanding Machine Learning: Concepts and Foundations

Understanding Machine Learning: Concepts and Foundations
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Course Modules

Unit 01 Overview Of Machine Learning

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Unit 02 Application To Machine Learning & Its Life Cycle

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Unit 03 Data Preprocessing In Machine Learning

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Unit 04 Classification Algorithms In Machine Learning

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Unit 05 Linear Regression Algorithm In Machine Learning

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Unit 06 Logistic Regression Algorithm In Machine Learning

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Unit 07 Support Vector Machine Algorithm In Machine Learning

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Unit 08 Kernel Tricks In Svm In Machine Learning

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Unit 09 Decision Tree Classification Algorithm In Machine Learning

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Unit 10 Random Forest Classification Algorithm In Machine Learning

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Unit 11 K-nearest Neighbour Algorithm In Machine Learning

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Unit 12 NaÏve Bayes' Algorithm In Machine Learning

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Unit 13 K-means Clustering In Machine Learning

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Unit 14 Reinforcement Learning (rl) Algorithm In Machine Learning

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Understanding Machine Learning: Concepts and Foundations

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About This Course

This course introduces the fundamentals of Machine Learning, including data types, data preprocessing, regression, classification, clustering, ensemble learning, Support Vector Machines, Decision Trees, K-Nearest Neighbours, Naïve Bayes, Random Forest, and Reinforcement Learning. It equips learners with practical skills to prepare data, build and evaluate Machine Learning models, implement algorithms using Python, and develop effective real-world predictive solutions.

Learning Objectives:

By the end of the course, you will be able to:

  • Explain key concepts, types, features, and applications of Machine Learning.
  • Understand and apply the complete Machine Learning life cycle.
  • Prepare, preprocess, analyze, and scale different types of data.
  • Apply regression, classification, clustering, and ensemble learning algorithms.
  • Evaluate Machine Learning models using appropriate performance metrics.
  • Implement major Machine Learning algorithms using Python.
  • Understand the fundamentals of Reinforcement Learning and its applications.

Why This Course Matters:

  • Develops analytical and problem-solving skills for data-driven challenges.
  • Builds expertise in data preprocessing, feature scaling, and model development.
  • Provides practical knowledge of regression and classification techniques.
  • Introduces major algorithms such as SVM, Decision Trees, KNN, Naïve Bayes, and Random Forest.
  • Develops understanding of clustering and unsupervised learning techniques.
  • Builds knowledge of ensemble learning and advanced predictive modeling.
  • Introduces Reinforcement Learning and intelligent decision-making systems.
  • Provides hands-on experience through Python-based algorithm implementation.
  • Improves model evaluation, interpretation, and predictive analysis skills.