Machine Learning Foundation

Machine Learning (ML) is a different approach where computer learns the rules of solving complex problems without being explicity programmed.

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Course Objectives

  • Introduce Machine Learning with a holistic approach
  • Discuss high-level theory of popular Machine Learning Algorithms
  • Hands-on coding of popular ML algorithms on classic data sets
  • Access the knowledge through the International Association of Business Analytics (IABAC™) framework

Course Module

  • What is Machine Learning
  • Applications of Machine Learning
  • Machine Learning vs Artificial Intelligence
  • Machine Learning Languages and platforms
  • Machine Learning vs Statistical Modelling

  • Popular Machine Learning Algorithms
  • Clustering, Classification and Regression
  • Supervised vs Unsupervised Learning
  • Application of Supervised Learning Algorithms
  • Application of Unsupervised Learning Algorithms
  • Overview of modeling Machine Learning Algorithm : Train , Evaluation and Testing.
  • How to choose Machine Learning Algoritham

  • Simple Linear Regression : Theory, Implementing in Python (and R), Working on use case
  • Multiple Linear Regression : Theory, Implementing in Python (and R), Working on use case
  • K-Nearest Neighbors : Theory, Implementing in Python (and R), KNN advantages, Working on use case
  • Decision Trees : Theory, Implementing in Python (and R), Decision |Tree Pros and Cons, Working on use case
  • Random Forests : Theory, Implementing in Python (and R), Reliability of Random Forests, Working on use case

  • Naive Bayes Classifier: Theory, Implementing in Python (and R), Why Naive Bayes is simple yet powerful, Working on use case
  • Support Vector Machines: Theory,Support vector machines with Python and R, Improving the performance with Kernals, Working on use case
  • Association Rules: Theory, Implementing in Python (and R),Working on use case
  • Model Evaluation: Overfitting & Underfitting
  • Understanding Different Evaluation Models

  • K-Means Clustering: Theory, Euclidean Distance method.
  • K-Means hands on with Python (and R)
  • K-Means Advantages & Disadvantages
  • Hierarchical Clustering : Theory
  • Hierarchical Clustering with Python (and R)
  • Hierarchical Advantages & Disadvantages

  • Dimensionality Reduction: Feature Extraction & Selection
  • Principal Component Analysis (PCA) : Theory, Eigen Vectors
  • PCA example with Python (and R) with use case
  • Advantages of Dimensionality Reduction
  • Application of Dimensinality Reduction with case study
  • Collaborative Filtering & Its Challenges

Learning Approch

  • Designed with a focus on real world relevance
  • Real-world case studies
  • Hands-on Assignments
  • Individualized Instructor
  • Career Guidance

Certification (LP Certificate)

Earn a Course Completion Certificate, an official Learners Point credential that confirms that you have successfully completed a course with us.

Certification (KHDA)

Earn a KHDA attested Course Certificate. The Knowledge and Human Development Authority (KHDA) is the educational quality assurance and regulatory authority of the Government of Dubai, United Arab Emirates.

Expert Instructors & Teaching Methods

Learners Experience

Frequently Asked Questions

Our trainings are mostly instructor led and classroom based. However, we also offer high quality live and interactive online sessions.

Our highly skilled faculties from around the globe are experts in their fields and come with decades of diverse industry experience. Our trainers are internationally recognized and locally preferred with rich research driven experience which will ensure highly customizable, engaging and top in the class learning experience.

Choosing LearnersPoint is true value for money as our trainings are right blend of theory and practice and specially developed keeping tomorrow's business needs in mind. All the training sessions are closely monitored through specially designed progress tracker to ensure reinforced and unparalleled learning experience.

Once you have registered for a course and wish to not to proceed with the training for any reason, you are entitled for a refund of the fees provided we are notified in writing within 2 days from the date of initial registration before the training commencement. The refunds are processed within 4 weeks from the day of withdrawal.