AI IT Course

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)    

Type	Learning Mode	Classes Schedule & Timing
1	For Regular Learning	Timing 10 AM to 3PM (Sunday/Govt & Local Holiday –  Holiday)   [All Health Courses Applicable for Regular]
2	For Part-time Learning	Saturday Only (Timing 10AM to 4PM)  [Note: Health Dept Course only ]
3	For Distance Learning	Sequencely 7 days classes – only (Timing 10AM to 4 PM) [Note: Except Health Dept Course]
4	For Online Learning	Zoom Class (Monday to Friday) & Meeting Discuss ( Timing 11 AM to 1PM) [Note: Except Health Dept Course]

 

Course Overview:

          These short programs are ideal for individuals seeking a quick entry point into the field of AI and ML, or for professionals looking to upskill and integrate AI capabilities into their existing roles. They often emphasize practical skills and hands-on experience to enable learners to start building and deploying AI solutions.

 

Course Responsibilities:-

• Designing and Implementing AI Models: 

This includes developing and deploying machine learning models, deep learning architectures, and other AI systems using Python libraries like TensorFlow, PyTorch, and Keras.

• Data Handling and Preprocessing: 

Tasks involve collecting, cleaning, transforming, and preparing large datasets for use in AI models, often utilizing Python libraries such as Pandas and NumPy.

• Model Training and Evaluation: 

Individuals are responsible for training AI models with prepared data, optimizing hyperparameters, and evaluating model performance using various metrics and techniques.

• Feature Engineering: 

This involves creating new features from existing data to improve the performance and accuracy of AI models.

• Integration and Deployment: 

Responsibilities may include integrating AI models into existing products or services and deploying them into production environments.

• Optimization and Performance Tuning: 

This involves optimizing AI systems for better accuracy, efficiency, and resource utilization.

• Problem Solving and Application: 

Applying AI techniques to solve real-world problems in various domains like natural language processing, computer vision, and predictive analytics.

• Collaboration and Communication: 

Working effectively within teams, documenting code, and communicating technical concepts to both technical and non-technical stakeholders.

• Staying Updated: 

Continuously learning about new AI algorithms, techniques, and Python libraries to keep skills current in a rapidly evolving field.

 

 Course Curriculum Components:

• Python Fundamentals: 

Mastery of Python programming, including data types, control flow, functions, object-oriented programming, and working with essential libraries like NumPy and Pandas for data manipulation.

• Machine Learning: 

Introduction to machine learning concepts, algorithms (e.g., linear regression, classification, clustering), and practical implementation using libraries like Scikit-learn.

• Deep Learning: 

Understanding neural networks, deep learning architectures (e.g., CNNs, RNNs), and building and training models with frameworks such as TensorFlow or PyTorch.

• Natural Language Processing (NLP): 

Techniques for processing and analyzing text data, including tokenization, sentiment analysis, and building NLP models for tasks like text classification or named entity recognition.

• Computer Vision: 

Working with image and video data, implementing computer vision algorithms for tasks like image recognition, object detection, and image processing.

• AI Application Development: 

Building AI-powered applications, potentially including chatbots, recommendation systems, or predictive models, and understanding how to integrate AI components into larger systems.

• Project-Based Learning: 

Hands-on experience through real-world projects to apply learned concepts and build a portfolio of AI projects.

• Tools and Libraries: 

Proficiency in using key AI-related Python libraries and tools, including Jupyter Notebooks, Scikit-learn, TensorFlow, PyTorch, Pandas, and NumPy.

These diplomas are suitable for aspiring AI engineers, data scientists, machine learning developers, or anyone looking to leverage AI in their field and gain a recognized certification in AI development using Python.

 

• Job opportunities: Data Analyst, Data Scientist, Machine Learning Engineer, and AI Engineer. Graduates can also pursue roles like Business Intelligence Analyst, AI Consultant, and Computer Vision Engineer, with job prospects in various sectors like finance, healthcare, and e-commerce

 

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.

1. Further Clarification Contact: 88701 91125 , 96299 01300 , 73582 18375