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-analytics-ai-course and diploma-in-data-science-and-ai 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: Programming: Proficiency in languages like Python, R, and SQL for data analysis and model building through python-ai-diploma-course.
- 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 included in courses/ai-it-course.
- 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. Interested candidates can apply for admission to get placement support.
Further Clarification Contact: 88701 91125 , 96299 01300 , 73582 18375 — for more details contact us.
