uLektz Skills

Learning Path

Non-Linear: Random Order

About the Course

Course Objectives

Understand AI Fundamentals: Provide a comprehensive understanding of AI concepts, techniques, and applications.
Develop Technical Proficiency: Equip students with the skills to design and implement AI systems and algorithms.
Promote Ethical AI Practices: Encourage ethical considerations and responsible use of AI technologies.
Prepare for Advanced AI Topics: Build a strong foundation for advanced study and research in AI.

Learning Outcomes

AI Concepts: Gain a deep understanding of machine learning, neural networks, natural language processing, computer vision, and robotics.
Technical Implementation: Learn to design, implement, and evaluate AI models and systems using programming languages such as Python and libraries like TensorFlow and PyTorch.
Data Analysis: Develop skills in data collection, preprocessing, and analysis to support AI projects.
Ethical AI: Understand the ethical implications of AI and how to implement AI solutions responsibly.

Skills Developed

Programming Skills: Proficiency in programming languages such as Python, R, and Java.
Machine Learning: Ability to develop and apply machine learning algorithms and models.
Data Science: Skills in data manipulation, analysis, and visualization.
Problem-Solving: Analytical and critical thinking skills to solve complex problems using AI.
Communication: Effectively communicate AI concepts, results, and implications to diverse audiences.
Project Management: Manage and execute AI projects from conception to deployment.

Career Path

AI Engineer: Design and implement AI algorithms and systems.
Data Scientist: Analyze data to extract insights and support decision-making.
Machine Learning Engineer: Develop machine learning models and algorithms.
AI Researcher: Conduct research in AI to advance the field and develop new techniques.
Robotics Engineer: Design and develop robotic systems that leverage AI.
AI Consultant: Advise organizations on implementing AI solutions and strategies.
Ethics Specialist: Focus on the ethical implications and responsible use of AI technologies

Course Study Materials

Introduction to Artificial Intelligence

Definition and History of AI
Types of AI narrow vs. general AI
AI applications and impact on various industries
Unit1 Test 15 Questions

Intelligent Agents and Problem Solving

Agent Architecutres
Search algorithms uninformed and informed search
Constraint satisfaction problems
Unit2 Test 20 Questions

Knowledge Representation and Reasoning

Propositional and first-order logic
Inference and theorem proving
Probabilistic reasoning and Bayesian networks
Unit3 Test 20 Questions

Machine Learning Fundamentals

Supervised learning classification and regression
Unsupervised learning clustering and dimensionality reduction
Reinforcement learning basics
Evaluation metrics and model selection
Unit 4 Test 20 Questions

Neural Networks and Deep Learning

Perceptrons and multilayer networks
Backpropagation and optimization algorithms
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs) and LSTMs
Unit5 Test 20 Questions

Natural Language Processing

Text preprocessing and feature extraction
Syntax and parsing
Sentiment analysis and text classification
Introduction to language models and transformers
Unit6 Test 20 Questions

Computer Vision

Image Processing fundamentals
Object detection and recognition
Face recognition
Image segmentation
Unit7 Test 20 Questions

Robotics and Perception

Robot Kinematics and dynamics
Simultaneous Localization and Mapping (SLAM)
Path planning and navigation

Expert Systems and Knowledge-Based AI

Rule-based systems
Fuzzy logic
Ontologies and semantic networks

Evolutionary Computation

Preparing operating budgets
Cash budgets
Flexible budgets
Forecasting techniques

AI for Game Playing

Minimax algorithm
Alpha-beta pruning
Monte Carlo Tree Search

Ethics and Societal Impact of AI

Bias and fairness in AI systems
Privacy concerns and data protection
AI governance and regulation
AIs impact on employment and society

Current Trends and Future Directions in AI

Explainable AI
AI in edge computing and IoT
Quantum computing for AI
Artificial General Intelligence (AGI) concepts

AI Project Development and Best Practices

AI project lifecycle
Data collection and preprocessing
Model deployment and maintenance
AI development tools and frameworks

The certificate issued for the Course will have

Student's Name
Photograph
Course Title
Certificate Number
Date of Course Completion
Name(s) and Logo(s) of the Certifying Bodies

Only the e-certificate will be made available. No Hard copies. The certificates issued by The Academic Council of uLektz. can be e-verifiable at www.ulektzskills.com/verify.

Students will be assessed both at the end of each module and at the end of the Course.
Students scoring a minimum of 50% in the assessments are considered for Certifications