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Artificial Intelligence (AI)

Applied Data Science and Machine Learning

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Start date

Spring 2023



10 Weeks


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This course is professionally designed by professionals for individuals who want to break into cutting-edge Artificial Intelligence (AI) and take advantage of AI and its related technologies while building new applications from scratch or thinking of enabling the legacy applications to leverage the power of AI.

This course starts from the basics of Artificial Intelligence and takes you step by step into the more advanced topics with hands on exercises, real-world examples, and problem solving with respect to industrial usage and best practices.

In this course, you will learn the foundations of Artificial Intelligence, Neural Networks and Deep Learning with hands on examples in Python and some introduction to market ready products and how to integrate them with new and legacy applications.

Entry Requirements

Participants must understand any high-level programming language and are familiar with AI and Machine Learning terms and technologies.

Course Level

Expert Level / Undergraduate / Postgraduate


Practical assignment at the end of the course resulting in certification

Accreditation / Awarding Body


What will you learn

  • Introduction to Artificial Intelligence
  • Fundamentals of Artificial Intelligence
  • Applications of Artificial Intelligence
  • Future of Artificial Intelligence
  • Neural Network Introduction (Intuition behind Artificial Intelligence)
  • Practical examples (Introduction to Numpy, Pandas and sci- kitlearn)

  • Building block for Neural Networks
  • Single NN
  • Input/output Mapping
  • Type Activation Functions
  • Neural Network Architecture
  • Practical examples (Introduction to ANN APIs and libraries)

  • EDA / Data wrangling
  • Back Propagation
  • Loss Functions
  • Hyperparameter Optimization
  • Gradient

  • Convolutional Neural Network
  • Computer Vision real life application
  • Overfitting/Underfitting
  • NN issues (vanishing gradients etc)
  • Model improvement and generalization techniques (Data Augmentation, Dropout, batch Normalization etc.)

  • Recurrent Neural Network
  • RNN variants (LSTMs etc)
  • BI basic idea and importance

Upon completion of this course, participants will be able to:                    

    • Understand the fundamental concepts of Artificial Intelligence (AI), Neural Networks, Deep Learning, Natural Language Processing etc.
    • Build, train, and deploy different types of predictive models with respect to industry best practices and large-scale real- world application requirements.
    • Application of Deep Learning to real-world scenarios such as object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers.
    • Master Deep Learning at scale with accelerated hardware and GPUs.              
    • Use of popular Deep Learning libraries such as Keras, PyTorch, and Tensorflow applied to industry problems.
    • Integrate the AI based predictive models into professional applications for real time predictions.

  • BI tools and techniques (Power BI hands on)
  • Introduction to Flask APIs
  • Practical Project

  • BI tools and techniques (Power BI hands on)
  • Introduction to Flask APIs
  • Practical Project

  • Natural Language Processing and its essential libraries e.g. NLTK
  • Tokenizing Text, Filtering Stop words, stemming and lemmatization
  • Basic of Part of Speech, Word Embedding
  • PCA
  • Project Mid quires and discussion

  • Time series prediction
  • Feature Engineering
  • Feature Extraction
  • Feature Importance

  • Big Data introduction and basic
  • Offline/Online warehouse
  • Project final evaluation and discussion

About the Instructors

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Sarah Jones

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John Doe

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John Doe

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Course Benefits


Gain practical skills and knowledge

Increase employability

Enhance career prospects

Keep up with industry trends

Build a professional network

Pursue further education