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Artificial Intelligence (AI)

Applied Data Science and Machine Learning

Designed for

Start Date

Spring 2021

Price

$

Duration

10 Weeks (100 hours)

Start Date

Spring 2021

Price

$

Duration

10 Weeks (100 hours)

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.

Course Name

Class Timings

Course Location

Course Level

Assessment

Accreditation/Awarding Body

Entry Requirements

Artificial Intelligence (AI)

5 hours class for 10 Weeks (Saturday and Sunday)

Online/Blended

Expert Level / Undergraduate / Postgraduate

Practical assignment at the end of the course resulting in certification

ICE

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

Course Name

Artificial Intelligence (AI)

Class Timings

5 hours class for 10 Weeks (Saturday and Sunday)

Course Location

Online/Blended

Course Level

Expert Level/ Undergraduate / Postgraduate

Assessment

Practical assignment at the end of the course resulting in certification.

Accreditation/Awarding Body

ICE

Entry Requirements

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

Topics

The list below provides a overview of the topics covered in this course.

  • 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
  • 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
  • OLAP/OLTP
  • Project final evaluation and discussion

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.

Tutors

All leading professionals and academics from across the globe 

Certificate-Icon.png

Certificate

Upon completion of the Ethical Hacking & Penetration Testing course, you will also receive the certificate awarded by ICE

All certificate images are for illustrative purposes only and may be subject to change at the discretion of ICE.

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