Program in Artificial Intelligence and Machine Learning

The AI-ML program covers essential topics like Statistics, Machine Learning, Deep Learning, Natural Language Processing, and Reinforcement Learning. Live sessions by global practitioners, labs, and industry projects are all incorporated into this program through our interactive learning model. Aims at acquiring industry-valued skills and the most commonly used tools and techniques.

Course Overview

Program in Artificial Intelligence & Machine Learning has been designed to upskill students from various academic background with essential mathematics and programming enabling students to have a strong foundation to learn AI & ML with ease. The curriculum is not just academic in nature but provides hands-on learning approach with latest industry practices. You will learn how big data is collected, cleaned and used in machine learning algorithms to make prediction for decision-making & problem-solving. You will also learn fundamentals of deep learning using neural networks to build algorithm that find the best way to perform task on their own.

Training Key Features

24/7 support and LMS Access
Hands-on experience with latest tools & applied projects
Live engagement classes by seasoned academics and professionals
Internship/Projects
Flexible timing for working professionals
EMI option

What You Will Learn

You will learn essential mathematics for AIML such as linear algebra, statistics, probability & statistic tools
You will learn Python programming & libraries (NumPy, Pandas & Matplotlib) essential for data science
You will learn the fundamentals of building machine learning models using TensorFlow & Scikit Learn You will learn an elective of your choice, Natural Language Processing (NLP) or Computer Vision (CV)

Knowledge and Human Development Authority (KHDA)

Airtics Education has been approved by the Knowledge and Human Development Authority (KHDA), the regulatory and quality assurance body that oversees private education across Dubai. The KHDA regulates teachers’ curricula, inspects educational institutes, and, most importantly, makes sure that all the students of the United Arab Emirates are receiving the education they need. Airtics Education complies with all guidelines proposed by KHDA for approval. Airtics Education is well aligned with the UAE government’s Vision 2021 to develop a first-rate education system by promoting transformation through quality digital education.

Who Can Apply for the Course?

Any graduate who has a keen interest in Artificial Intelligence
Individuals working in eCommerce, Healthcare, Engineering, Finance, Education, Marketing & Retail
Professionals aiming to upskill their career for better job opportunities
Professionals who wish to transition to roles such as Data Scientist, ML Engineer or AI Product Manager

Skills Covered

Tools/ Frameworks/ Libraries

IDE Shell

Automated Machine Learning Models

Application And Use Cases

eCommerce: AI in eCommerce can predict behavior patterns to provide recommendations to users
Banking: AI helps banks predict future outcome & trends, identify fraud, predict risk allotting loans
Marketing & Sales: AI helps businesses to market & sell product & service to the right audience.
Natural Language Processing: AI enables machines to understand the complex human language.
Health Care: AI diagnose, detect & notify abnormality quickly for faster treatments thus saving lives
Forecasting: AI forecasting can consume real time data with higher accuracy minimizing risk & loss.
Manufacturing: AI helps to make on budget products & on time delivery more accurately than humans
Education: AI enables universal access to personalized learning helping students improve weak spots
Retail: AI helps to improve demand forecasting, pricing decisions & optimize product placement

Eligibility

Bachelor’s Degree from a recognized University

Prerequisites

Due to its involvement in modern Machine Learning algorithms with math and programming, candidates having knowledge of linear algebra, probability and calculus could be a plus.

Course Module

Si. No.	Module name	Module Content	Learning Outcomes
Module 1	Basics of Python	Content Covered Basic Python Programming Variable and data types Conditional statements Loops Functions Essential Python libraries for data science Numpy matplotlib Setting up Python for Machine Learning	Learn basic concepts of Python Acquire rudimentary skills to write programs in Python Ability to use Python for Data Science & Machine learning Get application-ready with essential Python libraries & tools
Module 2	Mathematics & Statistics for Machine Learning & Artificial Intelligence	Content Covered Linear Algebra Statistics Probability Theory Statistical Tools (CSV, Excel)	Master the mathematical foundation required for writing programs Learn mathematical and statistical foundations required for AI & ML Acquire mathematical knowledge to build algorithms for data analysing Apply statistical analysis techniques using essential softwares on data sets
Module 3	Python for Machine Learning	Content Covered Python Programming for AI & ML Essential Python libraries for data analysis Data storage and manipulation by NumPy Data Visualization using Matplotlib Data Analysis with Pandas Basic introduction to Sci-kit-learn	Acquire practical skills in data analyzing, handling & visualization using Python tools Perform mathematical operations on a wide range of data using NumPy Operate Pandas to sort through and rearrange data, run analyses, and build data frames Ability to analyze by visualizing data with Matplotlib
Module 4	Introduction to Machine Learning & Artificial Intelligence	Content Covered Introduction to Machine Learning & AI Supervised Learning Unsupervised Learning Reinforcement Learning Machine Learning Algorithms (Regression, Classifiers, Clustering) Machine Learning Task (dataset, data cleaning, algorithm selection, training & testing model)	Understand Artificial Intelligence and Machine Learning fundamentals Demonstrate a comprehensive knowledge of the nature of the data and techniques used for pre- processing the data for machine learning Introduction to major machine learning algorithms like Classifiers (for image, spam, fraud), Regression (stock price, housing price, etc.), Clustering (unsupervised classifiers) Demonstrate a deep critical understanding of algorithms and mathematics behind established ML approaches
Specialization Module 1	Advanced Python for NLP	Content Covered Core Python for computer vision Strings Regex Machine Learning algorithms Regression KNN SVM Computer vision tools Keras TensorFlow	Understand basic concepts and standard tools used in NLP Acquire the prerequisite Python skills to move into Natural Language Processing Understand NLP python packages to enable them to write scripts for text pre-processing Learn popular machine learning algorithms, Feature Selection, and the Mathematical intuition behind them
Specialization Module 2	Machine Learning for NLP	Content Covered Introduction to machine learning Supervised learning Unsupervised learning ML deployment Automated speech recognition Text-to-speech conversion Decision theory Regression Classification Text Analysis applications Feed-forward neural networks Recurrent neural network Convolutional neural network Utterance classification Sequence tagging	Concepts of deep learning to build artificial neural networks and traverse layers of data abstraction, and get a solid understanding of deep learning using TensorFlow and Keras Understanding text processing and vectorization for ML Use case Develop and build fully automated NLP algorithms in Burt and transformers . Understand the concepts of NLP, feature engineering, natural language generation, automated speech recognition, speech-to-text conversion, text-to-speech conversion
OR
Specialization Module 1	Advanced Python for Computer Vision (CV)	Content Covered Core Python for computer vision Strings Regex Machine Learning algorithms Regression KNN SVM Computer vision tools Keras TensorFlow	Understand the Basic python tools used for Computer Vision Understand image processing python packages to enable them to write scripts for text pre-processing Learn popular machine learning algorithms, Feature Selection, and Mathematical intuition behind it Understand basic concepts and standard tools used in computer vision
Specialization Module 2	Machine Learning for Computer Vision (CV)	Content Covered Introduction to Computer Vision (CV) Deep Learning Network Models Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs) Introduction to Keras Model Life-Cycle Image Data Manipulation using Pillow Python library. Convert Images to NumPy Arrays and Back	Concepts of deep learning to build artificial neural networks and traverse layers of data abstraction and get a solid understanding of deep learning Develop and build fully automated CV algorithms USING YOLO Develop the usage of Deep learning models like CNN and RNN Gain insights about advancements in CV, AI, and Machine Learning techniques
Capstone Project	PG Level Project/Dissertation	Content Covered Clarifying the terms of the research Suggesting areas of reading Apply the knowledge base and abilities taught throughout the course to a real-world scenario The Problem, Understanding It, and Getting Data Frame a business issue in a manner that can be solved with AI & ML Apply Exploratory Data Analysis and Modeling Identify the methodology or algorithm that will handle the proposed challenge Reviewing the proposed methodology Establishing a research timetable, including initial dates for further meetings between the student and supervisor Advising students about appropriate standards & conventions concerning the assessment. Providing means of contact in addition to tutorials Educate learners to research and write their results and thoughts correctly, clearly, logically, and to a high-professional degree	Conduct independent research and development within the context of an AL & ML project Produce detailed documentation to a standard expected of a professional in the field of AI & ML Communicate technical information clearly and succinctly to a broad, non-specialist audience Apply knowledge of research principles and methods to plan and execute a researchbased industry project with a high level of personal autonomy and accountability