Artificial Intelligence Foundation

Course details

OVERVIEW: Artificial Intelligence (AI) is rapidly permeating every industry and has profound effects on virtually every aspect of our existence. The Artificial Intelligence Foundation course has been designed to provide professionals with a comprehensive understanding of AI and its applications.

CAREER OPPORTUNITIES:

Market Trends: Artificial intelligence (AI) is revolutionizing the tech industry with its disruptive innovations. According to IDC, the global AI market’s revenue is projected to reach $ billion by the end of 2021, an increase of more than 16%. By 2024, the company expects revenues to exceed $500 billion. As per the US Bureau of Labor Statistics, AI is a lucrative field with above-average job growth of 22 %, creating numerous job opportunities for professionals with AI Foundation certification.

Salary Trends: AI is a fast-evolving profession showing a 100% increase in AI and ML job postings between 2015 and 2018 with nearly 31% of organizations using it around the world. Being one of the highest-paying professions in the world, AI Engineers and AI Architects are among the highest-paid job roles. In the UAE, the average gross salary of an Artificial Intelligence Engineer is AED 337,935 per annum.

Demand and Opportunities: The demand for professionals with Artificial Intelligence Foundation certification is very high as AI jobs rank among the world’s top 10 most in-demand jobs. According to Gartner, AI-related fields are anticipated to create about million jobs around the world by end of 2021. As hiring growth for these jobs has risen by 32% since 2019, this sector is certain to remain in high demand for professionals.

COURSE OUTLINE:

Introduction to Artificial Intelligence

• History of Artificial Intelligence (AI)
• Five domains of AI
• Why AI now?
• Limitation of AI

Machine Learning Primer

• Machine Learning Primer
• Machine Learning core concepts, scalable algorithms, project workflow.
• Objective Functions and Regularization
• Understanding the Objective Function of ML Algorithms
• Metrics, Evaluation Methods, and Optimizers
• Popular Metrics in Detail: R2 Score, RMSE, Cross-Entropy, Precision, Recall, F1 Score, ROC – AUC, SGD, ADAM
• Artificial Neural Network
• ANN in detail, Forward Pass and Back Propagation
• Machine Learning Vs Deep Learning
• Core difference b/w ML and DL from an implementation perspective

Advanced Python for Deep Learning

• Python Programming Primer
• Installing Python, Programming Basics, Native Data types
• Class, Inheritance, and Magic Functions
• Python Classes, Inheritance Concepts, Magic Functions
• Special Functions in Python
• Overview, Array, selecting data, Slicing, Iterating, Array Manipulations, Stacking, Splitting arrays, Key Functions
• Decorators and Special Functions
• Decorators implementation with class
• Context Manager ‘with’ in Python
• Context Manager Application
• Exception Handling
• Try and Catch block
• Python Package Management
• Bundling and export python packages
• Tensor Flow and Keras for Deep Learning
• TensorFlow Basics
• TensorFlow core concepts, Tensors, core APIs
• Concrete Functions, Data Types, Control Statements
• Polymorphic Functions, Concrete Functions, Datatypes, Control Statements, NumPy, Pandas
• Autograph eager execution
• autograph implementation
• Keras (TensorFlow Built–in API) Overview
• Sequential Models, configuring layers, loading data, train and test, complex models, callbacks, save and restore Neural Network weights
• Building Neural Networks in Keras
• Building Neural networks from scratch in Keras

Mathematics for Deep Learning

• Linear Algebra
• Vectors, Matrices, Linear Transformation, Eigen Vectors, Matrix Operations, Special Matrices
• Calculus – Derivatives: Calculus essentials, Derivatives, and Partial Derivatives, Chain Rule, Derivatives of special functions
• Probability Essentials: Probability basics and notations, Conditional probability, Essential Probability theorems for Machine Learning
• Special functions: Relu, Sigmoid, SoftMax, Popular Loss Functions – Cross-Entropy, Quadratic Loss Functions

Deep Learning Foundation

• Deep Learning Network Concepts
• Core concepts of Deep Learning Networks
• Deep Dive into Activation Functions
• Building simple Deep Learning Network
• Tuning Deep Learning Network

KEY TAKEAWAYS:

Successful completion of our Artificial Intelligence Foundation course will enable the candidates to:

• Understand the concepts of AI and their applications
• Develop programming and statistical skills, as well as learn database concepts
• Identify and describe machine learning, deep learning, and neural networks
• Examine ethical concerns surrounding AI
• Analyze the impact of AI on business

ACCREDITATION:

Our Artificial Intelligence Foundation training course is accredited by ISO 9001: 2015 and the Knowledge and Human Development Authority (KHDA).

ALUMNI: Our academy graduates are successfully placed in some of the top-notch companies in Dubai like Jumeirah, Aramex, Airarabia, Damac, Emaar, etc. They are entrepreneurs, career changers, and lifelong learners.

WHY LEARNER’S POINT:

Being one of the foremost Artificial Intelligence Foundation course providers in Dubai, we have substantial experience in teaching relevant skills and knowledge.
Our AI Foundation training course offers the following benefits:

• Our AI Foundation course is designed based on real-world scenarios, formulating and evaluating practical requirements
• Our AI courses strive to provide a reality check and insight into the requirements of a challenging role and how to fulfill it
• We also provide live interactive sessions and hands-on experience through assignments associated with the actual work environment
• To enhance the learning experience of our students we provide organized group sessions and integrated training modules
• Our discrete approach to career guidance enables our students to get successfully placed in renowned organizations

Updated on 22 August, 2024