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Machine Learning Foundation

Course Summary

Machine Learning (ML) is a different approach where computer learns the rules of solving complex problems without being explicity programmed. Machine Learning algorithms are at the core and important pieces of data science. In recent years, Machine Learning has taken over a mainstream business and evolved has a career track by itself. A quick search in job portals reveals about 20,000 Machine Learning job opportunities on a daily basis in the USA alone. This course lays a solid foundation for ML aspirants with high-level theory and concept along with hands-on coding of popular Machine Learning algorithms: Linear and Logistic Regression, K-means clustering, SVM (Support Vector Machines), KNN (K -Nearest Neighbours) and Neural Networks.

This course - Machine Learning Foundation, is designed to provide a holistic understanding of various ML algorithms with high level theory and hands on application of ML algorithms to classis data sets.


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 Outline

Machine Learning Introduction

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

Machine Learning Algorithms

  • 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

Supervised Learning I

  • 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

Supervised Learning II

  • 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

Unsupervised Learning II

  • 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

  • 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

Target Audience

This course is a foundation level course, so most of the aspiring Machine Learning candidates can opt for this course

  • Professionals aspiring to pursue a career in Machine Learning or Data Science in general
  • Fresh college graduates, who are looking to career options in Data Science
  • Senior professionals, who want to gain a solid foundation on Machine Learning to manager Data Science projects
  • Candidates pursuing Data Scientist tracks

Prerequisites

  • Introduce Machine Learning with wholistic 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 International Association of Business Analytics (IABAC™) framework

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 Outline

Machine Learning Introduction

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

Machine Learning Algorithms

  • 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

Supervised Learning I

  • 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

Supervised Learning II

  • 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

Unsupervised Learning II

  • 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

  • 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

Target Audience

This course is a foundation level course, so most of the aspiring Machine Learning candidates can opt for this course

  • Professionals aspiring to pursue a career in Machine Learning or Data Science in general
  • Fresh college graduates, who are looking to career options in Data Science
  • Senior professionals, who want to gain a solid foundation on Machine Learning to manager Data Science projects
  • Candidates pursuing Data Scientist tracks

Prerequisites

  • Introduce Machine Learning with wholistic 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 International Association of Business Analytics (IABAC™) framework

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