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Course Description
Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks that typically require human intelligence, such as
Learning: AI systems can learn from data and improve their performance over time.Reasoning: AI systems can draw conclusions and make decisions based on the data they've learned.Problem-solving: AI systems can solve complex problems and adapt to new situations.Machine Learning (ML) is a subset of Artificial Intelligence that involves training algorithms to learn from data without being explicitly programmed. In other words, ML enables machines to improve their performance on a task by learning from experience.
ML algorithms are trained on large datasets and can identify patterns, classify data, and make predictions.
What you'll learn in this course?
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At the end of Intro to AI & ML training course, participants will be able to
- Apply AI/ML methods, techniques and tools to the applications
- Explore the practical components of developing AI apps and platforms
Course Curriculum
• Formulating real world problems as AI and ML problems
• Classification and Regression Problems
• Intuitive and Simple Algorithms: KNN, Decsion Tree and Simple Linear Classifier
• Representation of the world and real data: Emphasis on Text, Image, Speech and Sequences
• Visualization, Data Preparation and Unsupervised Learning
• End to end Problem Solving: Navigating through three specific problems and case studies
• Simple Linear Algorithms, Optimization and Training
• Non linear Solutions and MLP
• Gradient Descent and Backpropagation
• Decision Tree, Random Forest and Ensembles
• Principles and Practice of ML
• Support Vector Machines and Kernels
• Introduction to DL and Toolchain
• Convolutional Neural Networks
• Auto Encoders
• Recurrent Neural Networks
• Selected Special Topics
• Human In the Loop Solutions and Deployment
LEARN AT YOUR OWN PACE
Training Options
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