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2Month/20 Hour Price:45,000
40,000
Computer Science for Artificial Intelligence Professional Certificate
The Computer Science for Artificial Intelligence Professional Certificate is designed to equip learners with the core principles of computer science that drive the development of Artificial Intelligence (AI). This course emphasizes key concepts like algorithms, data structures, programming, and AI-specific computer science skills. Learners will gain the technical foundation to build AI models, applications, and systems. The course also covers essential AI tools and frameworks, ensuring that students can apply their knowledge in real-world AI environments.
Course Objectives:
Understand fundamental computer science concepts, including algorithms, data structures, and programming
Learn to apply computer science principles in the development of AI systems
Gain proficiency in Python programming for AI
Explore AI-specific concepts, including search algorithms, neural networks, and natural language processing (NLP)
Utilize AI frameworks and tools like TensorFlow, PyTorch, and Scikit-learn
Build AI models and applications using real-world datasets
Learn to apply computer science principles in the development of AI systems
Gain proficiency in Python programming for AI
Explore AI-specific concepts, including search algorithms, neural networks, and natural language processing (NLP)
Utilize AI frameworks and tools like TensorFlow, PyTorch, and Scikit-learn
Build AI models and applications using real-world datasets
Course Content
Module 1: Introduction to Computer Science
Overview of Computer Science:What is Computer Science?
Role of Computer Science in AI development
Role of Computer Science in AI development
Programming Basics with Python:
Python fundamentals (data types, control structures, functions)
Basic data manipulation and I/O operations
Basic data manipulation and I/O operations
Algorithms and Complexity:
Introduction to algorithms
Time complexity and Big-O notation
Time complexity and Big-O notation
Module 2: Data Structures for AI
Arrays, Lists, and Strings:Working with arrays and lists in Python
String manipulation for data processing.
Trees and Graphs:
Trees, binary search trees, and tree traversal algorithms
Graphs and graph algorithms (BFS, DFS)
Graphs and graph algorithms (BFS, DFS)
Data Structures in AI Applications:
How data structures are used in AI systems
Case studies of data structures in Machine Learning and AI
Case studies of data structures in Machine Learning and AI
Module 3: AI Algorithms
Search Algorithms:
Depth-first search (DFS), breadth-first search (BFS)
Heuristic search: A* and greedy algorithms
Heuristic search: A* and greedy algorithms
Optimization Techniques:
Gradient descent and its application in Machine Learning
Introduction to dynamic programming
Introduction to dynamic programming
Introduction to AI Problem Solving:
Problem-solving in AI using search algorithms
Applications of AI algorithms in game playing, robotics, and decision-making
Applications of AI algorithms in game playing, robotics, and decision-making
Module 4: Machine Learning Foundations
Supervised Learning:
Regression, classification, and decision trees
Implementing supervised learning algorithms using Scikit-learn
Implementing supervised learning algorithms using Scikit-learn
Unsupervised Learning:
Clustering algorithms: K-means, hierarchical clustering
Principal Component Analysis (PCA) and dimensionality reduction
Principal Component Analysis (PCA) and dimensionality reduction
Neural Networks and Deep Learning:
Introduction to artificial neural networks
Deep learning concepts (layers, activation functions, backpropagation)
Building neural networks using TensorFlow or PyTorch
Deep learning concepts (layers, activation functions, backpropagation)
Building neural networks using TensorFlow or PyTorch
Module 5: Natural Language Processing (NLP)
Overview of NLP:
Text processing and tokenization
Part-of-speech tagging, named entity recognition, and parsing
Part-of-speech tagging, named entity recognition, and parsing
NLP Algorithms and Models:
Bag of words, TF-IDF, and Word2Vec models
Using pre-trained models for text analysis
Using pre-trained models for text analysis
Sentiment Analysis and Chatbots:Building a simple sentiment analysis model
Introduction to conversational AI and chatbots
Module 6: AI Tools and Frameworks
Introduction to TensorFlow and PyTorch:
Setting up AI frameworks
Tensor operations and building models in TensorFlow and PyTorch
Practical AI Development:
Hands-on experience with building, training, and deploying AI models
Working with datasets, hyperparameters, and model evaluation
Working with datasets, hyperparameters, and model evaluation
Advanced AI Frameworks:
Using Keras for rapid AI model development
Integration with cloud-based AI platforms (Google AI, AWS, Azure AI)
Integration with cloud-based AI platforms (Google AI, AWS, Azure AI)
Module 7: Ethics and Responsible AI
Ethics in AI Development:
AI bias, fairness, and transparency
Understanding the ethical implications of AI systems
Understanding the ethical implications of AI systems
Regulations and Governance
Legal frameworks around AI
Global AI governance and accountability
Global AI governance and accountability
Building Ethical AI Models:
Strategies for mitigating bias in AI models
Implementing responsible AI practice
Implementing responsible AI practice
Career Path After Completion:
Upon completion of the Computer Science for AI Professional Certificate, learners will be prepared for various roles, including:
AI Engineer: Develop AI models and systems for automation and decision-making.Data Scientist: Utilize data science techniques to build predictive models and solve complex problems
.
Machine Learning Engineer: Design and deploy machine learning algorithms to optimize business processes.
Machine Learning Engineer: Design and deploy machine learning algorithms to optimize business processes.
AI Researcher: Explore advanced AI topics and contribute to AI research and development.
Software Engineer (AI Focus): Develop AI-driven applications and software solutions.
International Student Fees: USD200$
Job Interview Preparation (Soft Skills Questions & Answers)
Tough Open-Ended Job Interview QuestionsWhat to Wear for Best Job Interview Attire
Job Interview Question- What are You Passionate About?
How to Prepare for a Job Promotion Interview
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Flexible Class Options
Week End Classes For Professionals SAT | SUNCorporate Group Training Available
Online Classes – Live Virtual Class (L.V.C), Online Training
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