In this certificate you will learn essential concepts and skills needed to develop effective AI systems. The hands-on projects will give you a practical working knowledge of Machine Learning libraries and Deep Learning frameworks. You will also complete an in-depth Capstone Project, where you’ll apply your AI and Neural Network skills to a real-world challenge and demonstrate your newfound abilities to prospective employers.


Admissions Open Now


6 Months, 520 hours
20 hours / 4 per day


Total Fee: PKR 30,000

Per Month: PKR 5000

Home >> Academics >> Certificate Courses >> Artificial Intelligence Certificate

About Artificial Intelligence Certificate

This program focuses on the fundamental building blocks you will need to learn in order to become an Artificial Intelligence (AI) practitioner. Specifically, you will learn programming skills, and essential math for building an AI architecture. You’ll even dive into machine learning algorithms, neural networks and deep learning framework. One of our main goals at National Skills University is to help you create a job-ready portfolio. Building a project is one of the best ways to test the skills you’ve acquired, and to demonstrate your newfound abilities to prospective employers. In this certificate program you will be able to learn how to apply popular machine learning and deep learning libraries to industry problems involving object recognition, computer vision, image and video processing, text analytics, natural language processing (NLP), recommender systems, and other types of classifiers. In the sections below, you’ll find detailed descriptions of the projects, along with the course material that presents the skills required to complete them.

Who should join the AI Certificate Program?

Every aspiring person who wants to be a part of AI & ML World. This course is recommended for undergraduates looking to get into the AI career. The program assures individuals to gain education and necessary training to become successful in the AI-powered world. This learning kit prepares for skill development and provides job-ready capabilities for applicants looking to move into IT industry.

Skills You Will Learn

Successful completion of this certificate will provide you the right balance of knowledge, skills, attitude, and work experiences, an essential element of employability in the industry.

  • Knowledge about Linux Operating system and Machine learning frameworks
  • Master the Tools and Languages (Python, Anaconda, Jupyter Notebook, Numpy, Pandas)
  • Artificial Intelligence & Machine Learning for Trading
  • Linear Algebra & Calculus
  • Cognitive Technologies

Tasks you can perform (Competencies) on certificate completion

  • Write Python code to analyze, manipulate and visualize data
  • Learn how to build real-world Artificial Intelligence applications with Python
  • Discover how to build intelligent applications centered on images, text data
  • See how to use deep learning algorithms and build applications based on it
  • Learn how AI-powered chatbot technology works and its applications
  • Gain the practical know-how needed to apply ML techniques to new real life situations and problems

Total Course Duration (6 Months)

Weekly Class Hours

20 hours/4 per day

Total Contact Hours

520 hours


30% Theory, 70% Practical

Career Prospects

Successful completion of this certificate may lead to employment in a variety of different occupations and industries.

  • Healthcare
  • Education
  • Agriculture
  • Construction
  • Banking
  • Marketing
  • E-Commerce

Skills Oriented Learning Modules

(6 weeks)

In this Project you will be testing your newly acquired python coding skills by using a trained image classifier. You will need to use the trained neural network to classify images of dogs (by breeds) and compare the output with the known dog breed classification. You will have a chance to build your own functions, use command line arguments, test the runtime of the code, create a dictionary of lists, and more.

  • Learn why we program.
  • Prepare for the course ahead with a detailed topic overview.
  • Understand how programming in Python is unique.
  • Understand how data types and operators are the building blocks for programming in Python.
  • Use the following data types: integers, floats, Booleans, strings, lists, tuples, sets, dictionaries.
  • Use the following operators: arithmetic, assignment comparison, logical, membership, identity
  • Implement decision-making in your code with conditionals.
  • Repeat code with for and while loops.
  • Exit a loop with break, and skip an iteration of a loop with continue.
  • Use helpful built-in functions like zip and enumerate.
  • Construct lists in a natural way with list comprehensions
  • Write your own functions to encapsulate a series of commands.
  • Understand variable scope, i.e., which parts of a program variables can be referenced from.
  • Make functions easier to use with proper documentation.
  • Use lambda expressions, iterators, and generators.
  • Write and run scripts locally on your computer.
  • Work with raw input from users.
  • Read and write files, handle errors, and import local scripts.
  • Use modules from the Python standard library and from third-party libraries./li>
  • Use online resources to help solve problems
  • Object Oriented programming provides a few benefits over procedural programming. Learn the basics by understanding how to use Classes

(5 weeks)

Learn how to use all the key tools for working with data in Python: Jupyter Notebooks, NumPy, Anaconda, Pandas, and Matplotlib.

  • Organizing and Analyzing Data
  • Learn how to use Anaconda to manage packages and environments for use with Python.
  • Learn how to use Jupyter Notebooks to create documents combining code, text, images, and more.
  • Learn the value of NumPy and how to use it to manipulate data for AI problems.
  • Mini-Project: Use NumPy to mean normalize an ndarray and separate it into several smaller ndarrays.
  • Learn to use Pandas to load and process data for machine learning problems.
  • Mini-Project: Use Pandas to plot and get statistics from stock data.
  • Learn how to use Matplotlib to choose appropriate plots for one and two variables based on the types of data you have.

(5 weeks)

Learn the foundational linear algebra you need for AI success: vectors, linear transformations, and matrices—as well as the linear algebra behind neural networks.

  • Learn the basics of the beautiful world of Linear Algebra and learn why it is such an important mathematical tool.
  • Learn about the basic building block of Linear Algebra
  • Learn how to scale and add vectors and how to visualize them in 2 and 3 dimensions.
  • Learn what a linear transformation is and how is it directly related to matrices. Learn how to apply the math and visualize the concept.
  • Learn about the world of Neural Networks and see how it relates directly to Linear Algebra
  • VECTORS LAB - Learn how to graph 2D and 3D vectors.
  • LINEAR COMBINATION LAB - Learn how to computationally determine a vector’s span and solve a simple system of equations.
  • LINEAR MAPPING LAB - Learn how to solve problems computationally using vectors and matrices.

(5 weeks)

Learn the foundations of calculus to understand how to train a neural network: plotting, derivatives, the chain rule, and more. See how these mathematical skills visually come to life with a neural network example.

  • Visualize the essence of calculus. Learn why it is such a powerful concept in mathematics
  • Learn about the derivative, one of the most important tools in calculus.
  • See how a derivative can measure the steepness of a function and why it is such an important indicator in the world of machine learning.
  • Learn how to find the derivative of a composition of two or more functions, a very important tool in training a neural network.
  • Learn more about derivatives while focusing on exponential and implicit functions.
  • Learn about the formal definition of a derivative through understanding limits
  • Learn about the inverse of a derivative: the integral
  • Learn more about the world of neural networks and see how it relates directly to calculus through an explicit example

(5 weeks)

Successful software developers need to know how to incorporate deep learning models into everyday applications. Any device with a camera will be using image classification, object detection, and face recognition, all based on deep learning models. In this project you will implement an image classification application. This application will train a deep learning model on a dataset of images. It will then use the trained model to classify new images. First you will develop your code in a Jupyter notebook to ensure your training implementation works well. Then, you will convert your code into a Python application that you will run from the command line of your system.

  • Acquire a solid foundation in deep learning and neural networks. Implement gradient descent and backpropagation in Python.
  • Learn about techniques for how to improve training of a neural network, such as: early stopping, regularization and dropout
  • Learn how to use PyTorch for building deep learning models


Ms. Huma Israr


August 04, 2021@ 10:03 AM