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CERTIFIED ARTIFICIAL INTELLIGENCE DEVELOPER | CAID

This course on Artificial Intelligence (AI) and Machine Learning (ML) provides in-depth theoretical knowledge coupled with extensive practical learning experiences, through hands-on exercises and proj...

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  • English

Course Description

This course on Artificial Intelligence (AI) and Machine Learning (ML) provides in-depth theoretical knowledge coupled with extensive practical learning experiences, through hands-on exercises and projects. Participants will gain proficiency in AI and ML techniques, learn to implement algorithms using Python programming and utilize industry-standard tools and software for real-world applications....

This course on Artificial Intelligence (AI) and Machine Learning (ML) provides in-depth theoretical knowledge coupled with extensive practical learning experiences, through hands-on exercises and projects. Participants will gain proficiency in AI and ML techniques, learn to implement algorithms using Python programming and utilize industry-standard tools and software for real-world applications. TOOLS: • Python • IDE • Jupyter Notebooks • PyCharm • NumPy Pandas • Matplotlib • Scikit-learn • TensorFlow or PyTorch CERTIFICATION: • Upon successful completion of the course and capstone & live project, participants will receive a certification in artificial Intelligence and Machine Learning with Pedestal Certification TIME COMMITMENT: 10-12 Hrs./ Week 3 Days/Week

What you’ll learn
  • Python Fundamentals Programming
  • Machine Learning Fundamentals
  • Artificial Intelligence Fundamentals
  • Deep Learning and Neural Networks
  • Natural Language Processing (NLP)
  • Real World AI Applications
  • Case Studies
  • Pre Interview Preparation
  • Live Project

Covering Topics

1
Week 1-2: Introduction to AI and ML
2
Week 3-5: Python Fundamentals for AI & ML
3
Week 6-9: Supervised Learning
4
Week 10-12: Unsupervised Learning
5
Week 13-15: Deep Learning and Neural Networks
6
Week 16-18: Natural Language Processing (NLP)
7
Week 19-21: Reinforcement Learning
8
Week 22-25: Real-World Applications of AI & ML
9
Week 26-28: Capstone and Live Project

Curriculum

      Week 1-2: Introduction to Al and ML

•	Overview of Artificial Intelligence and Machine Learning
•	Historical Perspective and Evolution of Al
•	Ethical and Social Implications of Al
•	Introduction to Python Programming for AI & ML
      Week 3-5: Python Fundamentals for AI & ML

• Basics of Python Programming Language
• Data Structures and Manipulation in Python
• Introduction to Libraries such as NumPy, Pandas and Matplotlib
• Hands-on Exercises using Jupyter Notebook
      Week 6-9: Supervised Learning

• Linear Regression
• Logistic Regression
• Decision Trees and Random Forests
• Support Vector Machines (SVM)
• Model Evaluation and Validation Techniques
• Practical Implementation using Scikit-learn and TensorFlow/Keras
      Week 10-12: Unsupervised Learning

• K-means Clustering
• Hierarchical Clustering
• Principal Component Analysis (PCA)
• Anomaly Detection Techniques
• Implementation with Scikit-learn and TensorFlow/Keras
      Week 13-15: Deep Learning and Neural Networks

• Artificial Neural Networks (ANN)
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Transfer Learning and Fine-tuning
• Hands-on Projects using TensorFlow/Keras and PyTorch
      Week 16-18: Natural Language Processing (NLP)

• Text Preprocessing Techniques
• Sentiment Analysis
• Named Entity Recognition (NER)
Sequence-to-Sequence Models for Machine Translation
• Implementation with NLTK, SpaCy, and TensorFlow/Keras
      Week 19-21: Reinforcement Learning

• Markov Decision Processes (MDP)
• Q-Learning and Deep Q-Networks (DQN)
• Policy Gradient Methods
• Applications in Robotics and Gaming
• Practical Implementation using OpenAl Gym and TensorFlow
      Week 22-25: Real-World Applications of AI & ML

•	Healthcare Informatics
•	Financial Forecasting and Trading
•	Autonomous Vehicles and Robotics
•	Recommender Systems
•	Image and Speech Recognition
      Week 26-28: Capstone & Live Project

• Apply knowledge and skills acquired throughout the course to complete a comprehensive capstone project.
• Work on a real-world problem statement using Al and ML techniques. Utilize Python
• Utilize Python programming and industry-standard tools and software.
• Present findings and solutions to peers and instructors.

Frequently Asked Questions

Yes, this course is designed to be accessible both online and offline. You can choose your preferred mode of learning based on your convenience and availability of internet connectivity.

No, previous programming experience is not necessary. We cater to learners of all levels, starting from the basics and gradually building your skills.

While no prior programming experience is required, a basic understanding of computer operations is recommended. Familiarity with concepts such as variables, data, and algorithms can be beneficial but is not mandatory. We'll cover these topics as part of the course curriculum to ensure everyone can follow along effectively.