Certification Course in AI

Overview

Credge introduces an exceptional AI certification program tailored for high school
students. Delve into AI’s intricacies, from foundational principles to advanced concepts
like Deep Learning, Keras, Tensorflow, Convolutional Neural Networks, and more. Apply newfound knowledge in real-world scenarios, exploring AI’s role in processing
and extracting speech meaning from natural language. Uncover the construction of bots, dissect communication processes between humans
and systems, and understand how information is interpreted and delivered. This comprehensive program unveils the dynamic realm of AI, empowering students to
grasp its applications, from cutting-edge technologies to practical implementations in
speech processing and system communication.

Why Choose this Program?

1. Acquire the century’s most sought-after skill, AI, opening doors to innovation and opportunities across diverse domains.
2. Tap into a job market with soaring demand for AI expertise, ensuring lucrative
   and fulfilling career prospects.
3. Explore AI applications in various industries, from healthcare to finance, making
   the AI certification course relevant and adaptable.
4. Propel innovation by mastering AI; this certification course not only imparts
   essential skills but also empowers you to drive transformative advancements
   across industries.

Salient Features of This Program

• Tailored for High Schoolers (8-12th Preferred)
• Committed Program Support
• Hands-on Project Learning
• Industry Expert Instruction

You Get A Completion Certificate

Upon successfully concluding the AI Certification Course, participants attain a
prestigious certificate, validating their AI mastery. This credential enhances career prospects, unlocking a myriad of opportunities in the field.

Course Curriculum

• Introduction to Python
• Learn how to manipulate datasets in Python using Pandas which is the most powerful library for data preparation and analysis.

• Introduction to Machine Learning
  • Regression (Linear, Multiple and Logistic)
  • Classification (K-NN, Naive Bayes) techniques
  • Decision Trees
  • K-means Clustering
  • Hierarchical Clustering
  • High-dimensional Clustering
  • Case Study

• Neural Network Basics
  • Deep Neural Networks
  • TensorFlow using Neural Networks & Deep Learning
  • Introduction to Keras , Pytorch
  • ANN
  • Case Study

• Deep Learning and Computer Vision
  • Convolutional Neural Networks
  • Keras Library for Deep Learning in Python
  • Pre-processing Image Data
  • Object and Face recognition using OpenCV
  • Case Study

• Natural Language Processing and Speech Recognition
  • Statistical NLP and text similarity
  • Text Summarization
  • Syntax and Parsing techniques
  • Semantics and Generation
  • Scikit Learn NLP imports
  • Case Study

• Reinforcement Learning
  • Value based methods
  • Q-learning
  • Policy-based methods