AI Engineering Program

Course Modality


Course Level


Course Time

56 Hrs

Course Language


Course Overview

This course introduces fundamental concepts of Artificial Intelligence (AI), Business Intelligence(BI), Machine Learning (ML) and Deep Learning (DL). The AI module also gives the brief introduction of database concepts with basic understanding of Python Programming language and data visualization techniques. The statistics module gives the understanding of use of statistics for data science. This course also introduces Amazon SageMaker services for building and deploying Machine Learning and Deep Learning applications on AWS cloud.  The Data Engineering module teach the students to build and create reports and dashboards using Power BI. This course is designed for students to get understanding of data scientists and software developer’s workload by building awareness of Fundamentals of AI and Amazon AWS. The course is designed as a blended learning experience that combines instructor-led training with hands-on exercises in the course which are based on learning modules.

Target Audience :

Students, working professionals having basic coding knowledge and fundamentals of AI,BDA,Cloud

Why The DataTech Labs ?

Self-Paced Online Video

A 360-degree learning approach that you can adapt to your learning style

A 360-degree learning

Engage and learn more with these live and highly-interactive classes alongside your peers

24/7 Teaching Assistance

24/7 Teaching Assistance Keep engaged with integrated teaching

Online Practice Labs

Projects provide you with sample work to show prospective employers.

Applied Projects

Real-world projects relevant to what you’re learning throughout the program

Learner Social Forums

A support team focused on helping you succeed alongside a peer community

Get In Touch

Course Curriculum

AI Fundamentals 

  • Introduction to the AWS Cloud 
  • AWS Core Services 
  • AWS Integrated Services 
  • AWS Architecture 
  • Pricing and Support 

Introduction to AI 

  • Difference between Narrow, General and Super AI 
  • Applications of AI Across Industries 
  • Opportunities in AI 
  • Principles of Machine Learning

Database Concepts 

  • Introduction of Database concepts 
  • Foundations of Database 

AI Programming Fundamentals – Python 

  • Introduction AI Programming 
  • Basics programming Python 
  • Intro – Algorithms like Sorting, Searching, Geometric and Graphing 

AI Statistics – Python Introduction 

  • Basics Statistics Concepts 

Data Visualization with Python 

  • Data Visualization Fundamentals 


  • Fundamentals of Statistics 
  • Basic statistics 
  • Fundamentals of data visualization 
  • Graph types 
  • Introduction to some of the popular tools used such as Tableau, QlikView, d3js, ggplot, Bokeh, Plotly, Pygal, Altair,  Geoplotlib  


Azure AI Fundamentals 

  • AI fundamentals  
  • Introduction to AI 
  • Machine Learning 
  • Computer Vision 
  • Natural Language Processing 
  • Conversational AI 

Data Engineering Fundamentals 

1: Get Started with Microsoft Data Analytics 

2: Prepare Data in Power BI 

3: Cleaning, Transforming, and Loading Data 

4: Designing a Data Model in Power BI 

5: Create Model Calculations using DAX in Power BI 

6: Optimize Model Performance 

7: Create Reports 

8: Create Dashboards 

9: Create Paginated Reports 

10: Perform Advanced Analytics 

11: Create and Manage Workspaces 

12: Manage Datasets in Power BI 

13: Row-level Security 

Practical Data Science with Amazon SageMaker
  • How to solve a real-world use case with machine learning and produce actionable results using Amazon SageMaker. 
  • How to use Amazon SageMaker to cover the different stages of the typical data science process, from analyzing and visualizing a data set, to preparing the data and feature engineering, down to the practical aspects of model building, training, tuning and deployment. 

Machine Learning 

  • Intro to ML and the ML Pipeline 
  • Introduction to Amazon SageMaker 
  • Problem formulation 
  • Data preprocessing 
  • Model training 
  • Model evaluation 
  • Feature Engineering and Model Tuning 
  • Model deployment 

Deep Learning

  • Introduction to Machine Learning​ 
  • Introduction to Deep Learning​ 

Lab 1: Spinning up an Amazon SageMaker notebook instance and running a multi-layer perceptron neural network model  

  • Introduction to MXNet on AWS​ 

Lab 2: Running a Convolutional Neural Network (CNN) model to predicting images using CIFAR 10 dataset 

  • Deploying Smart Applications on AWS​ 

Lab 3: Deploying a Deep Learning model for predicting images using AWS Lambda 


Recommended Exams

Exam AZ-204

Developing Solutions for Microsoft Azure