AI/ML Program
Become an AI/ML Engineer in 6 months with 4 months of hands-on experience on live projects. This program will help you understand the basics and advanced levels of knowledge to succeed in this field.
Hybrid Mode - Choose your own choice
Office Environment for both offline as well as Online Classes
One to one mentorship by Industry leaders who had been CTOs
Job Assistance, Excellent Past record of 94% success
As the premier class in AI/ML, we cover topics, from foundational principles to advanced techniques, ensuring students develop a deep understanding of these transformative technologies. Through hands-on projects and expert guidance, our course empowers individuals to become proficient AI/ML practitioners, ready to tackle complex problems and drive innovation.
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#Python for AI & ML, #Applied Statistics, #Supervised Learning, #Unsupervised Learning, #Model Selection & Tuning,
AI/ML Opportunity Insights
1 Million Jobs by 2026
As per World Economic Forum, In India, AI will generate 1 Mn job by 2026
Earn 10 Lac+ per annum
With 2 years of experience, participants are earning avg package of more than 10LPA
Skill Gap
Currently only 67% jobs in AI are being full-filled due to lack of skills
Enroll Now
Call us to reserve your spot in upcoming batches.
Next Batches
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Curriculum Designed by Industry Experts
1. Introduction to Machine Learning
Understand the fundamental concepts and definitions of machine learning.
4. Supervised Learning
Develop and evaluate supervised learning models including linear regression, logistic regression, and decision trees
7. Machine Learning in Production
Deploy machine learning models effectively in a production environment
10. Capstone Project and Career Readiness
Synthesize knowledge and skills acquired throughout the course to tackle a real-world machine learning problem
2. Python for Machine Learning
Master the basics of Python programming essential for machine learning
5. Unsupervised Learning
Implement unsupervised learning methods like clustering and dimensionality reduction to uncover patterns in data
8. Advanced Topics
Explore advanced machine learning techniques such as transfer learning, GANs, and complex reinforcement learning algorithms
11. Additional Considerations
Operate popular machine learning frameworks such as TensorFlow and PyTorch to build and train advanced models
3. Data Management
Acquire skills in effective data collection and preprocessing techniques to prepare data for analysis
6. Neural Networks and Deep Learning
Explain the architecture and functioning of different types of neural networks, including CNNs and RNNs
9. Ethical and Social Implications
Recognize and address ethical and social challenges in machine learning, including data bias and privacy concerns