AI Engineering Program
Course Modality
Online
Course Level
Intermediate
Course Time
56 Hrs
Course Language
English
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
- 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
