Course Prerequisites
To ensure you get the most out of our Machine Learning & Generative AI Bootcamp, we recommend that you have the following foundational knowledge and skills:
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Fundamental Knowledge of Python or Similar Programming Language: A basic understanding of Python or a similar programming language is essential. You should be familiar with fundamental programming concepts such as variables, data types, loops, and functions.
Target Audience for Machine Learning & Generative AI Bootcamp
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Introduction: The Machine Learning & Generative AI Bootcamp is a 15-day intensive course for individuals with fundamental Python skills, designed to boost proficiency in machine learning and generative AI techniques.
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Job Roles and Audience:
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Data Scientists
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Machine Learning Engineers
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AI Research Scientists
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Data Analysts
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Software Engineers
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IT Professionals
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Data Engineers
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Computer Vision Specialists
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NLP Specialists
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Research Scientists
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AI Enthusiasts
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Python Developers
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Data Science Students
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Technology Consultants
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Business Intelligence Analysts
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R&D Engineers
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Software Developers
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Robotics Engineers
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IT Project Managers
Learning Objectives - What you will Learn in this Machine Learning & Generative AI Bootcamp?
Machine Learning & Generative AI Bootcamp
This immersive 15-day course covers foundational and advanced topics in machine learning and generative AI, focusing on Python, data analysis, computer vision, predictive modeling, natural language processing, and generative models such as transformer-based approaches.
Learning Objectives and Outcomes:
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Understand Python for Data Analytics: Grasp Python basics, data types, control flows, OOPS concepts, and essential data science libraries like NumPy, Pandas, and Matplotlib.
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Master Computer Vision with OpenCV: Learn image processing, segmentation, object detection, and object tracking using OpenCV.
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Data Mining for Machine Learning: Acquire skills in Web Scraping, data acquisition, and the ethical considerations involved.
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Database Operations: Navigate SQL and Python database operations, and implement CRUD operations for machine learning projects.
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Document OCR using Deep Learning: Implement OCR from data acquisition to model training and evaluation using Deep Learning techniques.
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Predictive Modelling: Design, build, and evaluate predictive models for regression, classification, and clustering.
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Natural Language Processing (NLP): Utilize NLP libraries like NLTK and SpaCy for text processing, POS-tagging, named entity recognition, and vector
Target Audience for Machine Learning & Generative AI Bootcamp
-
Introduction: The Machine Learning & Generative AI Bootcamp is a 15-day intensive course for individuals with fundamental Python skills, designed to boost proficiency in machine learning and generative AI techniques.
-
Job Roles and Audience:
-
Data Scientists
-
Machine Learning Engineers
-
AI Research Scientists
-
Data Analysts
-
Software Engineers
-
IT Professionals
-
Data Engineers
-
Computer Vision Specialists
-
NLP Specialists
-
Research Scientists
-
AI Enthusiasts
-
Python Developers
-
Data Science Students
-
Technology Consultants
-
Business Intelligence Analysts
-
R&D Engineers
-
Software Developers
-
Robotics Engineers
-
IT Project Managers
Learning Objectives - What you will Learn in this Machine Learning & Generative AI Bootcamp?
Machine Learning & Generative AI Bootcamp
This immersive 15-day course covers foundational and advanced topics in machine learning and generative AI, focusing on Python, data analysis, computer vision, predictive modeling, natural language processing, and generative models such as transformer-based approaches.
Learning Objectives and Outcomes:
-
Understand Python for Data Analytics: Grasp Python basics, data types, control flows, OOPS concepts, and essential data science libraries like NumPy, Pandas, and Matplotlib.
-
Master Computer Vision with OpenCV: Learn image processing, segmentation, object detection, and object tracking using OpenCV.
-
Data Mining for Machine Learning: Acquire skills in Web Scraping, data acquisition, and the ethical considerations involved.
-
Database Operations: Navigate SQL and Python database operations, and implement CRUD operations for machine learning projects.
-
Document OCR using Deep Learning: Implement OCR from data acquisition to model training and evaluation using Deep Learning techniques.
-
Predictive Modelling: Design, build, and evaluate predictive models for regression, classification, and clustering.
-
Natural Language Processing (NLP): Utilize NLP libraries like NLTK and SpaCy for text processing, POS-tagging, named entity recognition, and vector
