Artificial Intelligence With Python for Teenagers
Design real-world AI & machine learning apps using Python
- Machine Learning , Artificial Intelligence and its Applications
- Natural Language Processing (NLP).
- Artificial Neural Network (ANN).
- Convolutional Neural Network (CNN).
- Recurrent Neural Network. (RCN)
- Implementing Deep Q-Learning using Tensorflow
Course Description
Artificial intelligence (AI) is the simulation of human intelligence in machines that have been programmed to think similar to and mimic actions of humans. These machines can learn from experience and perform human-like tasks.
This level will provide you with an in-depth understanding of Artificial Intelligence and Machine Learning through the use of several Python libraries. Also we will cover the various machine learning algorithms, including supervised, unsupervised, and reinforcement learning. Natural Language Processing (NLP), Neural Networks, TensorFlow, and Regression are some of the other important ideas covered in this module
This module will offer practical exposure covering real-world projects as well as knowledge of essential areas of modern AI.
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Mode
Online / Offline
Levels
3 levels
Level Duration
48 hours
This Course Includes:
- Suggested Age: 14+ Years
- Prerequisites: Python Advanced
- Location: Online and Classroom
- Mode of Delivery: Group Session and 1:1
- Batch Size: Maximum batch size upto 5 students per faculty for online classes
- Language: English
- Credits: End of term module completion certificate & Awards based on performance
Looking For Start Date?
1:1 starts on demand
Group batches starts several times during the years, please reach out for start dates.
Bonus Features
Completion Certificate
Performance Awards
Feedback From Instructors
1:1 Parents Teacher Meeting
Special One-Off Workshops
Entrepreneurial Skills
Detailed Curriculum
Level 1
- Getting Started with Machine Learning
- Applications, Best Python libraries or ML,
- Data and It’s Processing: Create Test DataSets using Sklearn, Generate test datasets, Data Preprocessing, Feature Scaling
- Supervised learning :Multiclass classification using scikit-learn, Gradient Descent, Linear Regression
- Python | Implementation of Polynomial Regression
- Softmax Regression using TensorFlow
- Logistic Regression
Level 2
- Naive Bayes Classifiers
- Support Vector
- Decision Tree
- Random Forest
- Unsupervised learning
- Reinforcement Learning
- Dimensionality Reduction
Level 3
- Natural Language Processing
- Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- GANs – Generative Adversarial Network
- Implementing Deep Q-Learning using Tensorflow
Projects You Will Work On
AI/ ML
Tensor flow
Scikit Learning
Kras
Learning Pathways Post This Module
Post completion of the module, Students can opt for these following related modules to further their learning.
Web Development : MERN STACK Development
Learn MERN Stack Development with React + Redux as Front End and Node + Express as Backend By doing Hands-On Projects
Java
Comprehensive Java programming course integrated with design principles, best methadologies & instructor-led projects
Students Portfolio
Our students have always enjoyed learning with us, as much as we have enjoyed teaching them. Check out the amazing projects created by them! Some amazing Projects created by our amazing students.
