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Artificial Intelligence Foundation
Program
Artificial Intelligence Foundation
Dive into the fundamentals of AI. Immerse yourself in the basic principles of data structure and data visualization. Develop a practical understanding of Python as an AI tool.
Build a solid foundation for your AI career and gain valuable skills.
Flexible Schedule
Beginner Level
Mentor Support
Estimated Time
4 hours/week
Artificial intelligence (AI) is forging ahead solving complex real-world problems; everything from self-driving cars, to facial recognition, to missile guidance, to tumour detection. This program covers the foundational requirements and applications needed for careers associated with AI and offers a broad introduction to the subject, including its history.
As you progress through the program, you will be introduced to the many technologies used within AI systems. You will be introduced to Python; a very popular AI programming language. You will explore the basics of relational databases and algorithms. And you will become familiar with the concepts and tools used for data visualization. You will also develop skills in Jupyter Notebook, Python, MongoDB, SQL, NoSQL, and creating data, which will prepare you for tackling the hands-on labs provided in the program.
Once you have learnt how to run your first Python program, you will then implement data structures, conditions, branching, looping, searching, and sorting in Python. You will discover how to set up a MongoDB cluster and work with relational databases. And you will work with various Python libraries, including pandas and NumPy, to visualize data in the form of charts and graphs.
For individuals keen to take their first step in the amazing world of AI, this Artificial Intelligence Program is an ideal place to start.
Artificial Intelligence Foundations Program comprises five purposely designed courses that take you on a carefully defined learning journey.
It is a self-paced program, which means it is not run to a fixed schedule with regard to completing modules or submitting assignments. To give you an idea of how long the course takes to complete, it is anticipated that if you work 4 hours per week, you will complete the program in 10 weeks. However, as long as the program is completed by the end of your enrollment, you can work at your own pace. And you will be encouraged to stay connected with your learning community and mentors through the course discussion space.
The materials for modules are accessible from the start of the program and will remain available for the duration of your enrollment. include videos, hands-on labs, exercises, and knowledge checks.
Enrollment in the program includes all five courses. You cannot enroll for the courses separately.
As part of our mentoring service you will have access to valuable guidance and support throughout the program. We provide a dedicated discussion space where you can ask questions, chat with your peers, and resolve issues. Depending on the payment plan you have chosen, you may also have access to live classes and webinars, which are an excellent opportunity to discuss problems with your mentor and ask questions. Mentoring services may vary package wise.
Once you have successfully completed Artificial Intelligence Foundations Program , you will earn your Certificate of Completion.
You will have:
This program is aligned with industry-approved occupational standards set by SSC NASSCOM. Once you have successfully completed this program, you will receive a cobranded FutureSkills Prime/SkillUp Online Certificate of Participation that confirms you have:
The national occupational standards to which this course is aligned relate to the following job roles:
Program Outline
Course Introduction
Program Syllabus
Grading Scheme
Lab Installation - Jupyter Labs
Final Exam Instructions
Reading: Module Introduction
Reading: The Evolution and Definition of AI
Reading: Early AI Technology
Reading: AI Pioneers
Infographic: AI Timeline and Famous Applications
Reading: Current AI Tech and Trend-setters
Reading: Key AI Concepts and Terminology
Reading: Opportunities and Occupations in AI
Video - Why Study Artificial Intelligence?
Exam 1.1 - 4 Questions
Reading: Module Introduction
Reading: The Difference Between Narrow, General, and Super AI
Exam 1.2 - 4 Questions
Reading: Module Introduction
Reading and Lab: Application of AI Across Industries
Reading: AI in Video and Image Processing
Reading and Lab: Uses of AI in Speech Processing and Intelligent Bots
Exam 1.3 - 4 Questions
Reading: Module Introduction
Reading: Opportunities in AI Across Industries
Reading: Basics of Deep Learning and Advanced Analytics
Reading: The Coming Ethical and Legal Issues Associated with AI
Exam 1.4 - 4 Questions
Reading: Module Introduction
Reading: Principles of Machine Learning
Video: What is Machine Learning?
Reading: Machine Learning Model
Video: Classification and Regression in Machine Learning
Exam 1.5 - 4 Questions
Final Exam - 15 Questions
Reading: Introduction to Database Concepts
Reading: Learning Objectives
Module 2.1 Exam - 3 Questions
Module 2.2 Lab Exercises
Reading: SQL Relational Database Models
Reading & Video: Types of SQL Relationships
Reading: Data Types and Data Formats
Lab: Define and Describe NoSQL Databases
Module 2.2 Exam - 8 Questions
Module 3 Lab Exercises
Reading: Selecting and Retrieving Data from a SQL Database
Reading & Labs: Filtering, Sorting and Calculating Data
Reading and Lab: Building Joins
Lab: Using Python for Data Acquisition and Analysis
Module 2.3 Exam - 5 Questions
Final Exam - 20 Questions
Reading: Python Course Introduction
Reading & Video: Advantages of Python in AI
Reading: Data Types
Video - Data Types
Lab: Data Types in Python
Reading: Understand and Define Fundamental Data Structures
Lab: Understand and Define Fundamental Data Structures
Reading: Implement and Apply Data Structures
Lab: Implement data structures
Exam 3.1 - 5 Questions
Reading: Basic Python Programming Introduction
Reading: String Operations
Lab - Strings in Python
Video - Data Structures
Reading: Demonstrate Lists and Tuples
Video: Demonstrate Lists and Tuples
Lab: Demonstrate Lists and Tuples
Reading: Operators
Lab: Operators and Operations
Reading: Demonstrate Conditions, Branching
Lab: Demonstrate Conditions and Branching
Reading: Loops - for,while
Lab: For Loops and While Loops
Reading: Functions, Classes, Objects
Lab: Functions, Classes, and Objects
Basic Python Scripts
Pandas and Numpy
Hands-on Lab - Pandas
Hands-on Lab - Numpy
Exam 3.2 - 5 Questions
Reading: Algorithms Module Introduction
Reading: Understand and Define Sorting Algorithms
Reading: Create and Apply Algorithms
Module 3.3 Exam - 4 Questions
Reading: Sorting Algorithms Module Introduction
Reading: Sorting Algorithms
Jupyter Notebook Labs: Quick Sort
Jupyter Notebook Labs: Insertion Sort
Exam 3.4 - 3 Questions
Reading: Searching Algorithms Module Introduction
Searching Algorithms
Jupyter Notebook Labs: Linear Search
Jupyter Notebook Labs: Binary Search
Exam 3.5 - 3 Questions
Reading: Geographic and Graphing Algorithms Module Introduction
Reading: Geometric Algorithms
Final Exam - 10 Questions
Reading: AI Statistics - Python Introduction
Reading: Getting Started with Cognos
Reading: Basic Statistic Concepts
Exam 4.1 - 3 Questions
Reading: Descriptive Statistics Introduction
Reading: Define and Demonstrate Descriptive Statistics
Hands-on Lab - Calculating central values
Reading: Define and Demonstrate Probability
Reading: Define and Demonstrate Correlation
Hands-on Lab - Correlation of Data
Reading: Define and Demonstrate Linear Regression
Reading: Describe Basic Statistical Analysis Tools
Reading: Descriptive Statistics Summary
Hands-on lab - Using plotly with built-in data
Exam 4.2 - 5 Questions
Final Exam - 10 Questions
Reading: Data Visualization Fundamentals Course Introduction
Reading: Understand and Define Simple Data Visualizations and Graph Types
Reading: Identify Popular Visualization Dashboards
Exam 5.1 - 4 Questions
Reading: Module Introduction
Reading: Define and Describe Basic Visualization Tools
Reading: Define Specialized Visualization Tools
Reading: Describe Creating Maps and Visualizing Geospatial Data
Exam 5.2 - 4 Questions
Reading: Module Introduction
Reading: Introduction to Building Charts
Lab: Build Pie Chart
Lab: Build Line Chart
Lab: Build Bar Chart
Lab: Build Column Graph
Lab: Build Ring Plot
Lab: Build Map Graph
Reading: Course Conclusion
Exam 5.3 - 4 Questions
Final Exam - 10 Questions
Instructions
Final Exam - 30 Questions
Certificate
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FAQs
Yes. Artificial Intelligence Foundations is 100% online. You will not be required to attend any classes in person. With this in mind, therefore, please note you will need appropriate access to the internet and the required technology to be able to use the program materials. The materials are in the form of articles, videos, and knowledge checks.
This means you can access this program wherever you live in the world. And the great news is that you wont be learning alone. You will be encouraged to connect with other learners and the program mentors through the program discussion space.
This program has been specifically designed for learners who have never worked in the field of artificial intelligence before; you do not need to have background experience at all. It therefore offers an excellent introduction to the subject. However, you will need basic math and statistical knowledge, some basic computers skills, and also some basic Python programming experience before you start the program. This will help you greatly as you progress.
Artificial Intelligence Foundations has been designed to run as self-paced program. This means you have complete flexibility on the order of the modules you work on. However, the program has been designed to introduce topics in a way that is of most use to the learner, because skills and concepts that are required in a later module in the program are introduced early on. Thus, the order of the modules that you will see set out in your dashboard is the order that we recommend you follow.
We would say yes. Python is a very useful language if you want to pursue a career in both AI and data science. This is because it is easy to learn and easy to use. Plus, many of the data analysis and visualization tasks required for data science are conducted using coding in Python. You will find, therefore, that many employers will advertise roles requiring coding skills in Python. So, if youre near the start of your programming journey, then Python is a great language to learn.
The great news is that one of the courses in this program introduces this language well - AI Programming Fundamentals: Python. By the end of this part of the program, you will have learnt the basics of this critical language in AI and will be able to apply what you have learned using Jupyter Notebook.
In the Artificial Intelligence Foundations program, you'll learn essential tools like the Python programming language and its key libraries, such as NumPy and Pandas. You'll also use Jupyter Notebook for coding and SQL for database querying. Excel will be covered for basic data organization and analysis. These tools will equip you with a solid foundation for AI and data science projects.
The Artificial Intelligence Foundations course will significantly enhance your career by equipping you with foundational AI skills that are highly sought-after in various industries. You'll gain practical knowledge of key tools and technologies such as Python, machine learning libraries, and data manipulation tools. This will make you proficient in handling real-world AI and data science projects. This expertise can open up opportunities for roles in AI development, data analysis, and machine learning engineering.
Artificial intelligence is a science and technology based on multiple disciplines. People who work in the field would consider computer science, linguistics, biology, mathematics, psychology, and engineering to be listed as the foundations of AI. However, data is also important to mention as one of the foundations of AI since all the practical and effective applications begin with data.
Type of certificate
Certificate of Completion
About this program
05 Courses
19 Modules
05 Skills
Includes
Discussion space
32 Hands - on labs
18 Videos
18 Module exams
05 Course exams
01 Final exam
Create
Jupyter notebook
Data analyzing project
Exercises to explore
Jupyter Notebook
MongoDB - SQL & NoSQL
Python
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