This comprehensive course covers the fundamentals of Artificial Intelligence (AI) and Machine Learning, diving deep into supervised, unsupervised and reinforcement learning algorithms, neural networks, deep learning, computer vision, natural language processing, generative models, time series forecasting and critical topics around AI safety and ethics. Through hands-on programming assignments using Python, NumPy, TensorFlow and more, you’ll gain practical experience building intelligent systems
Once enrolled, our friendly support team is here to help with any course-related inquiries.
Learning Outcomes
- Understand core AI/ML concepts and algorithms
- Build neural networks and master deep learning for vision and NLP
- Implement generative models, reinforcement learning, and time series analysis
Delivery Format
Instructor-led 40 hours course
Delivered online via live virtual sessions
3-day schedule, scheduled every month
Course Benefits
- Career Advancement
- Flexible Learning
- Continuous Learning Opportunities
- Mini-projects/Lab activity documents/Configuration guides
- Quizzes
- Programming-based assessment
- A Capstone project
- Industry Relevance
Instructors
All courses are taught by experienced trainers with a minimum of eight years of industry experience.
Modules
- Definition and scope of Artificial Intelligence
- Overview of AI and machine learning
- Historical perspective
- Current trends and applications
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Regression and classification
- K-nearest neighbors
- Decision trees
- Random forests
- Python programming language
- Libraries like NumPy, Pandas, and Matplotlib
- Linear regression
- Logistic regression
- Bagging
- Decision trees and random forests
- Support Vector Machines (SVM)
- K-means clustering
- Time Series Analysis
- Hierarchical clustering
- Principal Component Analysis (PCA)
- Neural network architectures
- Activation functions
- Forward and backward propagation
1. Introduction to deep learning
2. Stochastic gradient descent
1. Text preprocessing techniques
2. Word embeddings
3. Sequence-to-sequence models
1. Image processing
2. Convolutional Neural Networks (CNNs)
3. Object detection models like YOLO, SSD
1. Bias and fairness in AI systems
2. Interpretability and explainability
3. Adversarial attacks on neural networks
4. Privacy and security in AI
5. Understanding ethical considerations in AI
Prerequisites
- Basic programming skills (Python recommended)
- Familiarity with college-level math (linear algebra, calculus, probability/statistics)
Audience
This course is suitable for students, professionals or researchers looking to build a solid foundation in AI/ML theory and practice. Some modules require stronger math/programming backgrounds, but the course provides a comprehensive learning path for beginners to experts.
How do I Access The Program
- Buy the course online
- Save your payment transaction receipt for any future reference
- The programs will commence only upon formation of a minimum batch of 20 participants
Bulk Orders
Incase you are looking for bulk user licenses, or customized Learning Paths for various Job Roles, reach out to us with your detailed requirements.
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