Cloud Computing

Machine Learning Certification Course

Understand the utility of machine learning in the transformation of the digital world

In-Person Classroom

Unfortunately, this training model is not available for this certification

  • 2-days of guaranteed to run in-person training
  • Application assistance and support by certified staff
  • Exam passing tips and tricks to assist in the exam
  • 2 practice tests to gauge your learning post-training

$ 1899

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Live Online Classroom

Learn from the comfort of your home or office

  • 3-days of assured instructor-led online live training
  • Recorded lesson video for post-training learning
  • Exam passing tips and tricks to assist in the exam
  • 2 practice tests to gauge your learning post-training

$ 1799

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Online Self-Study

A learning model, prominently known for the flexibility it offers

  • 180 days of complete access to the complete course
  • Exam passing tips and tricks to assist in the exam
  • 2 practice tests to gauge your learning post-training
  • Application assistance and support by certified staff
  • Chapter end quizzes and exercises in all modules

$ 899

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Machine Learning Certification Course

Computers are becoming smarter, as AI & Machine Learning make tremendous strides in simulating human thinking.

Course Overview

The Machine Learning course consists of a total of at least 16 days of qualifying courses. At least one of the Machine Learning for Big Data and Text Processing courses is required. Those with prior machine learning experience may start with the Advanced course, and those without the relevant experience must start with the Foundations course and also take the Advanced course. Participants must attend the full duration of each course.

Course Agenda

  • The emergence of Artificial Intelligence

  • Recommender Systems

  • Relationship between Artificial Intelligence, Machine Learning, and Data Science

  • Definition and Features of Machine Learning

  • Machine Learning Approaches

  • Machine Learning Techniques

  • Applications of Machine Learning

  • Data Exploration

  • Seaborn

  • Data Wrangling

  • Missing Values in a Dataset

  • Data Manipulation

  • Supervised Learning Flow

  • Types of Supervised Learning

  • Types of Classification Algorithms

  • Types of Regression Algorithms

  • Accuracy Metrics

  • Cost Function

  • Evaluating Coefficients

  • Challenges in Prediction

  • Logistic Regression

  • Sigmoid Probability

  • Accuracy Matrix

  • Regression

  • Factor Analysis

  • Principal Component Analysis (PCA)

  • First Principal Component

  • Eigenvalues and PCA

  • Demo: Feature Reduction

  • Linear Discriminant Analysis

  • Maximum Separable Line

  • Overview of Classification

  • Classification Algorithms

  • Decision Tree

  • Random Forest Classifier - Bagging and Bootstrapping

  • Decision Tree and Random Forest Classifier

  • Demo: Horse Survival

  • Naive Bayes Classifier

  • Steps to Calculate Posterior Probability

  • Support Vector Machines

  • Linear SVM: Mathematical Representation

  • Non-linear SVMs

  • The Kernel Trick

  • Example and Applications of Unsupervised Learning

  • Clustering

  • Hierarchical Clustering

  • K-means Clustering

  • Optimal Number of Clusters

  • Time Series Pattern Types

  • White Noise

  • Stationarity

  • Removal of Non-Stationarity

  • Time Series Models

  • Steps in Time Series Forecasting

  • Ensemble Learning Methods

  • Working of AdaBoost

  • AdaBoost Algorithm and Flowchart

  • Gradient Boosting

  • XGBoost

  • Model Selection

  • Common Splitting Strategies

  • Purposes and Paradigm of Recommender Systems

  • Collaborative Filtering

  • Association Rule Mining

  • Association Rule Mining: Market Basket Analysis

  • Association Rule Generation: Apriori Algorithm