This course will enable learners to understand the necessity and areas of applications of linear statistical model techniques like Factor Analysis which is an integral part of the Exploratory Data Analysis.
At the end of the Machine Learning model creation process comes Model Operationalization in which the created model is launched and integrated within the organization's data management and data administration infrastructure. This course provides learners with an introduction to Model Operationalization and a layout of the process through a Machine Learning model is employed into daily use.
Model Validation techniques are used to assess the accuracy of Machine Learning models. This course provides learners with an introduction to commonly used Model Validation techniques and specific methods for for Regression, Classification, and Clustering.
Clustering is an Unsupervised Machine Learning technique, and is often utilized to differentiate between various levels of a variable. This course provides learners with an introduction to popular Clustering algorithms with an example of each executed using Python.
Classification is a Supervised Machine Learning technique, and is often utilized in decision based approaches. This course provides learners with an introduction to popular Classification algorithms with an example of each executed using Python.
Regression is the most commonly used Machine Learning paradigm, and is often utilized for comparing or establishing the relationship between two or more variables. This course provides learners with an introduction to popular Regression algorithms with an example of each executed using Python.
Machine Learning is a subset of Artificial Intelligence, which provides systems the ability to automatically learn and improve through experience. It is an up and coming branch of applied mathematics. Machine Learning for Beginners is an introductory level course, intended to provide learners with pre-requisites of R and Python Basics, Hypothesis Testing, Exploratory Data Analysis (EDA), and Feature Engineering.
Model validation is performed to identify the potential limitations and assumptions of created models, verify if they are performing as expected, and assess if their impact is in line with the originally designed objectives and business uses. This course contains information on various techniques which can be used to validate regression, classification, and clustering models.
This course enables learners to understand the various components of a time series and how various techniques use these components to generate a forecast. At the end of this course, learners will also be able to assess the accuracy of generated forecasts and understand areas where specific forecasting techniques can be applied.
This course will enable learners to understand the necessity and areas of applications of unsupervised machine learning algorithms like dimensionality reduction and principal component analysis (PCA).
This course will enable learners to get accustomed with advanced (supervised) machine learning algorithms like Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA) which overcome the shortcomings of conventional regression and classification algorithms.
This course will enable learners to get started with basic regression analysis. At the end of this course, learners will be able to understand the shortcomings of conventional regression techniques and know which alternative techniques can be used to overcome these limitations.
This course will enable learners to understand the key concepts of advanced regressions technique (LARS, MARS, etc.) which overcome the shortcomings of conventional regression techniques.
This course will enable learners to understand key concepts of various clustering techniques and their areas of application.
This course will enable learners to be accustomed with the concept of Decision Tree and related techniques. At the end of this course learners will also be able to understand how to use ensemble models for solving business problems.
This course will enable learners to get started with the basic classification techniques required to predict categorical values. At the end of this course, learners will also be able to determine areas where KNN, NBC and logistic regression can be applied and understand model evaluation techniques.