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ML-Practitioner

Certificate from

Webster University

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Machine Learning Engineer

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World-Class Instructor
Dr. Raju Pandey

Real-Life Applications
On AI-ML Lab 

Starts On

12-Weeks, Online
10-12 hours/week

USD 899.00
Introductory Offer

 

Machine Learning Landscape

Course Features

15 Case Studies, 22 Live-Labs: Programming Examples, 20 quizzes, 15 Tests, and 2 Real-World Applications.
 

High Demand

Companies globally are hiring Artificial Intelligence and Machine Learning experts aggressively in all sectors.
 

Bright Future

133 million unfilled AI/ML jobs projected by 2022. Unlike many others, AI/ML jobs are not going away.

Curriculum

Course-1: Foundations of AI, Data-Science, Statistics, Python and Tools for ML

  • Natural Intelligence and Types of Intelligence
  • Definitions, Types and Goals of AI
  • History of AI
  • Elements and Classification of AI
  • Applications of AI and Why should you learn AI?
  • Natural Learning and Machine Learning
  • ML Vs. Traditional Systems
  • Evolution, Implications and Future of AI
  • Matrices
  • Functions
  • Data Analysis
  • Data and Types
  • Control Flow
  • Functions
  • Object Oriented Programming
  • Modules & Packages
  • Representation of Data as Matrices
  • Vectors and Sequences
  • Indexing & Slicing
  • Transformation
  • Operations on Arrays
  • Read and Store Data in Different Formats (CSV, XLS, TXT, JSON, etc).
  • Series and Data Frames
  • Indexing
  • Operations
  • Handling Unknowns
  • Sorting
  • Storage
  • Introduction to Data Visualization
  • Charts, 3D Plots and Contours
  • Introduction to ML-Framework
  • Estimators
  • Dataset Libraries
  • Basics and Types of Data
  • Data Preparation
  • Data Preparation: Categorical Data
  • Data Normalization
  • Data Analysis
  • Feature Engineering
  • Cost and Error Analysis

Course-2: Machine Learning Engineer

This module will provide a high-level view of what machine learning is, and how it is being used to build novel applications.

  • Machine Learning: An Overview
  • Machine Learning Models and Applications
  • Machine Learning System Architecture
  • Machine Learning Challenges
Discuss the intuition behind regression algorithms, how they enable predictions, and analysis techniques.

  • Introduction:
  • Regression
  • Notation and Conventions
  • Solution Framework
  • Univariate Regression:
  • Linear Regression
  • Gradient Descent Algorithm
  • Regression Metrics
  • SciKit Regression Model:
  • SciKit for Linear Regression
  • Case Study 1: Horizontal Line
  • Case Study 2: Predict Fahrenheit Equation
  • Case Study 3: Predict Height of Building
  • Case Study 4: Predict House Prices (Univariate Model)
  • Multivariate Regression:
  • Model
  • GDA for Multi-Feature Applications
  • Normal Equation
  • Case Study 1: Predict House Prices (Multivariate Model)
  • Case Study 2: Predict House Prices (Multivariate Model with Scaling)
  • Polynomial Regression:
  • Model
  • Case Study: Predict House Prices (Polynomial Regression)
  • Bias and Variance
  • Regression with Regularization:
  • Regularization with SciKit
  • Case Study: Predict House Prices (Regularization)
  • Introduction:
  • Classification Schemes: Single Class, Multi-Class, Multi-Label, and Multi-Output Classifications
  • Metrics
  • Classification Algorithms:
  • Classification vs. Regression
  • Multi-Class Classification
  • Logistic Regression (Classification):
  • Introduction
  • Logistic Function
  • Cost Function
  • Gradient Descent Algorithm
  • Logistic Regression Model (SciKit)
  • Case Study 1: Classification Model for IRIS Data Set
  • Case Study 2 (Live Lab): Classification of MNIST Images
  • Summary and Assumptions
  • Support Vector Machines (Classification):
  • Introduction
  • SVM Cost Model: Distance Constraint, Hard Constraint and Soft Constraint
  • Gradient Descent Algorithm for SVM
  • SVM Kernels
  • SciKit SVM Model
  • Case Study 1: Classification Model for IRIS Data Set
  • Case Study 2: Classification Model for Cancer Tumor Data Set
  • Decision Tree (Classification):
  • Introduction
  • Classification Using Decision Tree
  • SciKit Decision Tree Model
  • Case Study: Classification Model for Diabetes Data Set
  • Introduction to Clustering
  • K-Means:
  • Algorithm
  • Algorithm Analysis
  • SciKit Clustering Models
  • Case Study 1: Clustering of IRIS Flower Dataset
  • Case Study 2: Image Compression
  • Hierarchical Clustering:
  • Algorithm
  • SciKit Clustering Model
  • Case Study: Hierarchical Clustering of Digital Image Dataset
  • Feature Engineering: Dimensionality Reduction (PCA):
  • Introduction to Principal Component Analysis (PCA)
  • PCA using Eigen Vectors and Eigen Values
  • Singular Value Decomposition
  • SciKit Model for Dimension Reduction:
  • Case Study 1: PCA on Synthetic Dataset
  • Case Study 2: PCA for Image Compression
  • Case Study 3: PCA for Noise Reduction
  • Validation and Testing:
  • Evaluation Methods
  • Testing ML Applications
  • Validating ML Applications:
  • Random Subsampling
  • Leave 1 or P-Out Cross Validation
  • K-Fold Cross Validation
  • SciKit Model for Cross Validation
  • Ensemble Learning:
  • Introduction
  • Bagging:
  • SciKit Model for Bagging
  • Case Study: Using Bagging Classifiers and Regressors on Synthetic Datasets
  • Boosting:
  • SciKit Model for Boosting
  • Case Study: Using Boosting Classifiers and Regressors on Synthetic Datasets
  • Random Forest:
  • SciKit Model for Random Forests
  • Case Study: Using Random Forest Classifiers and Regressors on Synthetic Datasets
  • Stacking:
  • SciKit Model for Stacking
  • Case Study: Using Stacking Regressors on Synthetic Datasets
  • Application 1: Sentiment Analysis:
  • Introduction
  • Input Data (Text) Analysis and Transformation
  • Feature Engineering
  • Case Study: Sentiment Analysis of Movie Review Data
  • Application 2: Stock Prices Prediction:
  • Introduction to Time Series Data
  • Support for Time Series Data in Python and Pandas
  • Analysis of Time Series Data
  • Prediction Algorithms:
  • Traditional Regression Algorithms (Linear, SVM, Decision Tree, etc).
  • ARIMA
  • Case Study: Prediction of Apple Stock Prices

Dr. Raju Pandey

Meet your faculty, Dr. Raju Pandey

  • Dr. Pandey’s research and entrepreneurial interests lie in AI, Programming Languages, Blockchain, and IoT. He has developed a novel software platform for building multi-platform AI, Blockchain, Mobile, and IoT applications. The platform includes a next-generation programming language, Ankur, that he has designed and implemented. It enables the development of AI applications in which both algorithm-driven (deterministic) and data-driven (nondeterministic) components can be integrated seamlessly
  • He has published 40+ papers in conferences and journals and holds 16+ patents in software, visualization, wireless networks, data analytics, security, and control systems
  • Currently CEO and founder of Thinking Books, a software Infrastructure and Tools company
  • Professor Emeritus in the Computer Science department at the University of California at Davis
  • Holds a B.Tech. degree in Computer Science from IIT (Indian Institute of Technology), Kharagpur, and Ph.D. in Computer Science from the University of Texas at Austin

Benefits

Jobs

Land a lucrative job as ML-engineer upon completing this course.
 

Certificate

Get a Certificate from Webster University, USA.

Future-proof

AI/ML is one of the few future-proof areas, with ever-increasing demand.

So what are you waiting for?

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Frequently Asked Questions and Answers

Who should take this course?

Anyone interested in developing knowledge in ML, a student seeking employment, by building a foundation in AI/ML or an employee interested in re‐skilling or up-skilling for a career growth or a teacher interested in becoming a trainer in AI/ML or a manager wanting to unlock new opportunities or to bring AI/ML into their products and offerings.

Are there any prerequisites for this course?

A basic understanding of computers and at least one programming with some (2-3 years) experience is highly recommended.

What are the equipment and technical requirements of this program?

The participants need to have a computer with Internet access, and an internet browser to access this course. No special software is required except for a PDF viewer.

What is required to successfully complete this course?

A participant must score at least 70%, in all the quizzes. We want you to do well in the quizzes, learn and benefit from the course. Once you are enrolled, you can refer to the video on “How to take the tests and do well in them?” in the orientation module.

In case you cannot do well in a quiz on the first attempt, no worries. You can go through the required sections of the course again and retake the quiz. You will get a maximum of 3 attempts to “pass” a quiz.

What are the duration and the time commitment required for this course?

Recommended duration of the course is 12 weeks. Students are expected to dedicate 120-130 hours in total to complete the course. This is a self-directed online program and hence you are free to decide when you want to study during these 12 weeks.
Note: You will have an additional 2 weeks to review what you have learned. That means that you will have a total of 14 weeks of access to the course content.

Who will I learn from?

You will learn from Dr. Raju Pandey who is Professor Emeritus in the Computer Science department at the University of California at Davis.
Following are some of the highlights of his accomplishments and background:

  • Currently CEO and founder of Thinking Books, a software Infrastructure and Tools company
  • Possesses Research and Entrepreneurial interests in AI, Programming Languages, Blockchain, and Internet of Things
  • Has published 40+ papers in conferences and journals and holds 16+ patents in software, visualization, wireless networks, data analytics, security, and control systems
  • Holds a B.Tech. degree in Computer Science from IIT (Indian Institute of Technology), Kharagpur, and Ph.D. in Computer Science from the University of Texas at Austin

Do I receive a certificate for this course?

Upon successful completion of the course, you will receive a certificate of completion from iZen, USA. You need to score at least 70% to successfully complete this course.

When and how do I receive the certificate?

You will receive a digital certificate within two weeks after your successful completion of the course. You can share this verifiable certificate on various social media platforms (e.g. Facebook, Twitter, LinkedIn, etc.).

What are the accepted payment methods for this course?

We accept all major credit and debit cards. You may also pay with PayPal. If you encounter any challenges please contact support@iZen.ai

Does the program fee include taxes?

Yes, the program fee is inclusive of any taxes with the exception of GST for countries like Singapore & India, etc.

What is your refund policy?

You may request a refund for any reason within 7 days after your enrollment. Once we process your refund, you will be automatically removed from the course. Please send all refund requests to support@iZen.ai

Do I need to buy any textbooks for this course?

Not necessary. All required resources are provided in this course and you can access them online.

Whom do I contact if I have more questions?

Please send all your queries to support@iZen.ai

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