Institute Agenda :-
Study Materials ,Bag , ID Card Provide & course Non - Semester Pattern. Exam Scheduled July/August Month, Before 2 Months informed to Exam Appear students for their Hall Ticket Register.
(Note: Anytime agenda can change on the Management Basis.)
Learning Mode:- (Selection Type)
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Type
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Learning Mode
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Classes Schedule & Timing
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1
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For Regular Learning
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Timing 10 AM to 3PM
(Sunday/Govt & Local Holiday – Holiday)
[All Health Courses Applicable for Regular]
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2
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For Part-time Learning
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Saturday Only (Timing 10AM to 4PM)
[Note: Health Dept Course only ]
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3
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For Distance Learning
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Sequencely 7 days classes – only
(Timing 10AM to 4 PM)
[Note: Except Health Dept Course]
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4
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For Online Learning
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Zoom Class (Monday to Friday)
& Meeting Discuss ( Timing 11 AM to 1PM)
[Note: Except Health Dept Course]
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Course Overview:
These programs typically cover topics like machine learning, data collection and analysis, and AI model development, often culminating in hands-on projects to prepare students for careers like data scientist or AI engineer. The duration can vary from a few months to two years, depending on the specific program and format (e.g., certificate vs. postgraduate diploma).
Course Responsibilities:-
Core responsibilities
- Data collection and preprocessing: Gathering and cleaning data from various sources to ensure its quality, accuracy, and consistency.
- Data analysis and interpretation: Analyzing complex data sets to identify patterns, trends, and insights to inform business decisions.
- Model development and deployment: Building, training, and deploying predictive models and machine learning algorithms.
- AI application development: Designing and building AI-based systems, such as chatbots, image recognition tools, and recommendation engines.
- Data visualization and reporting: Creating dashboards, reports, and visualizations to communicate findings and recommendations to both technical and non-technical audiences.
- Problem-solving: Applying data science and AI concepts to solve real-world problems and help businesses make data-driven decisions.
- Collaboration: Working with other data scientists, engineers, and teams to build and maintain data pipelines and integrate solutions.
Key skills developed through the diploma
- Programming: Proficiency in languages like Python, R, and SQL for data analysis and model building.
- Statistical analysis: A strong understanding of statistical concepts to interpret data effectively.
- Data management: Skills in collecting, cleaning, and managing large datasets.
- Machine learning: The ability to understand and apply machine learning algorithms.
- Tool proficiency: Experience with data science and visualization tools like Tableau and Power BI.
- Communication: Strong verbal and written communication skills for presenting findings
Course Curriculum Components:
Core technical components
- Programming and Tools:
- Python for AI and Data Science, including libraries like NumPy, Pandas, Matplotlib, and Scikit-learn.
- SQL and NoSQL databases for data storage and management.
- Data visualization tools like Tableau and Power BI.
- Data Handling and Preprocessing:
- Data cleaning, normalization, and standardization techniques.
- Feature engineering, selection, and dimensionality reduction.
- Handling structured, unstructured, and semi-structured data.
- Machine Learning:
- Supervised learning (regression, classification) and unsupervised learning (clustering).
- Ensemble methods like Random Forest and Gradient Boosting.
- Model evaluation metrics and techniques for preventing overfitting.
- Deep Learning:
- Neural network basics and backpropagation.
- Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
- Transfer learning, such as with models like BERT.
Advanced and applied components
- AI Fundamentals and Ethics:
- Introduction to artificial intelligence, intelligent agents, and search algorithms.
- Principles of responsible AI and ethical implications.
- Applied AI and Specializations:
- Natural Language Processing (NLP).
- Computer Vision (e.g., object detection, image processing).
- Generative AI and LLM applications.
- Deployment and Operations:
- Model deployment strategies, including using frameworks like Flask or FastAPI.
- Containerization with Docker.
- Cloud deployment on platforms like AWS, GCP, or Azure.
- Capstone and Projects:
- Real-world projects to apply learned skills.
- Job opportunities: Prompt Engineer, AI Chatbot Developer, and Content Creation Specialist
Features of the Course :
During Training Period, OJT at Hospitals/Industrial/Companies (If applicable courses only)
Placement Guidance:
Those who are Regular and Part time candidate Assurance the Placement 100% throughout India based on the candidates and other online and distance Candidates. We will guide to the Placement and based on the Candidate’s willing.
- Further Clarification Contact: 88701 91125 , 96299 01300 , 73582 18375
