Skip to main content

13 Courses

Artificial Intelligence & Machine Learning with Python
Artificial Intelligence
Preview Course

Artificial Intelligence

Artificial Intelligence & Machine Learning with Python

HRDC Reg. No: 10001463637
Duration: 35 hours (5 days)


Course Overview

This course offers a comprehensive introduction to Artificial Intelligence (AI) and Machine Learning (ML) using Python. Participants will explore essential concepts, implement algorithms, and analyze data using Python libraries. The program emphasizes hands-on learning with real-world applications, enabling participants to develop and deploy AI and ML models effectively.


Who Should Attend

  • Target Audience:

    • Aspiring data scientists and AI/ML enthusiasts
    • Software engineers and developers
    • Professionals transitioning to AI/ML roles
    • Students and graduates in computer science, engineering, or related fields
  • Target Industries:
    IT, healthcare, finance, retail, manufacturing, automotive, and other industries leveraging AI and ML

Learning Outcomes

By completing this course, participants will:

  1. Understand the fundamentals and applications of AI and ML.
  2. Use Python libraries like NumPy, Pandas, and Scikit-learn for data manipulation and analysis.
  3. Implement supervised and unsupervised learning algorithms.
  4. Evaluate and optimize machine learning models.
  5. Develop deep learning models with TensorFlow and Keras.
  6. Deploy AI models for real-world problems.
  7. Address ethical considerations in AI and ML.

Prerequisites

  • Basic Python programming knowledge
  • Familiarity with linear algebra, probability, and statistics

Lab Setup

  • Computer with at least 8GB RAM and modern processor
  • Anaconda distribution (Python, Jupyter Notebook)
  • Libraries: NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Keras

Teaching Methodology

  • Interactive lectures and presentations
  • Hands-on coding sessions
  • Case studies and collaborative projects
  • Quizzes and assessments
  • Final project for applied learning (optional)

  • (0)
  • Comprehensive Python Hands-on Workshop
    Artificial Intelligence
    Preview Course

    Artificial Intelligence

    Comprehensive Python Hands-on Workshop

    HRDC Reg. No: 10001464660
    Duration: 14 hours (2 days)


    Course Overview

    This workshop provides a comprehensive introduction to Python programming. Participants will learn Python's core concepts, syntax, data structures, and essential libraries through hands-on coding exercises. By the end of the workshop, they will have the skills to write Python scripts, develop basic applications, and understand Python's applications in data analysis, web development, and automation.


    Who Should Attend

    • Target Audience:

      • Beginners in programming
      • Professionals automating tasks or working with data
      • Developers expanding their programming skills with Python
    • Target Industries:
      IT, software development, data science, web development, automation

    Learning Outcomes

    By completing this workshop, participants will:

    1. Master Python syntax and semantics.
    2. Work with Python data structures (lists, tuples, dictionaries, sets).
    3. Implement loops, conditionals, and control flow constructs.
    4. Write reusable code using functions and modules.
    5. Handle file operations and manage exceptions.
    6. Use libraries like Pandas and Matplotlib for data manipulation and visualization.
    7. Understand basic Object-Oriented Programming (OOP) concepts.
    8. Develop and debug Python applications in an IDE.

    Prerequisites

    • No prior programming experience required.
    • Familiarity with text editors or IDEs (e.g., VSCode, PyCharm) is helpful.

    Lab Setup

    • Python 3.x installed on participant machines
    • IDE or code editor (VSCode, PyCharm, Jupyter Notebook)
    • Required libraries: Pandas, Matplotlib, NumPy (installable via pip)

    Teaching Methodology

    • Interactive lectures and live coding demonstrations
    • Hands-on exercises and coding challenges
    • Q&A sessions and peer discussions
    • Guided labs with step-by-step instructions

  • (0)
  • Data Science and AI ML Immersion Program
    Artificial Intelligence
    Preview Course

    Artificial Intelligence

    Data Science and AI ML Immersion Program

    HRDC Reg. No: 10001462528
    Duration: 14 hours (2 days)


    Course Overview

    This program introduces participants to the fundamentals of Data Science, Artificial Intelligence (AI), and Machine Learning (ML). It focuses on the methodologies, tools, and applications of supervised and unsupervised learning while exploring the contrasts between generative and non-generative AI systems. With hands-on demonstrations and real-world case studies, participants will gain the skills to integrate AI and ML for business optimization.


    Who Should Attend

    • Target Audience:

      • Business leaders
      • Project managers
      • Data scientists
      • Software developers
      • Analysts
      • Individuals interested in applying AI and ML to business
    • Target Industries:
      Technology, data science, business management, research and development


    Learning Outcomes

    By completing this course, participants will:

    1. Understand the fundamentals of Data Science and its business applications.
    2. Gain insights into supervised and unsupervised Machine Learning methodologies.
    3. Learn the differences between generative and non-generative AI.
    4. Explore practical ML use cases and tools like Python and R.
    5. Develop the ability to integrate AI and ML into real-world business challenges.

    Prerequisites

    • Basic understanding of data analytics or statistical concepts (helpful but not mandatory)
    • Enthusiasm and active participation

    Lab Setup

    • Access to Python and R environments
    • Sample datasets for hands-on exercises
    • Business case studies for practical applications

    Teaching Methodology

    • Interactive lectures and presentations
    • Hands-on demonstrations with real-world datasets
    • Practical exercises and quizzes
    • Case studies for applied learning

  • (0)
  • Data Scraping and Data Mining with Python
    Artificial Intelligence
    Preview Course

    Artificial Intelligence

    Data Scraping and Data Mining with Python

    HRDC Reg. No: 10001466994
    Duration: 21 hours (3 days)


    Course Overview

    This course provides an in-depth introduction to data scraping and mining using Python. Participants will explore libraries like Beautiful Soup, Scrapy, and Selenium for web scraping, and learn data mining techniques such as clustering, classification, and association rules. Hands-on sessions will focus on extracting, processing, and analyzing data to generate actionable insights for various industries.


    Who Should Attend

    • Target Audience:

      • Data analysts, scientists, and software developers
      • Researchers and professionals automating data collection
      • Individuals seeking to enhance data scraping and mining skills
    • Target Industries:
      Technology, finance, marketing, research, and industries using data-driven decision-making

    Learning Outcomes

    By completing this course, participants will:

    1. Understand data scraping fundamentals and ethical considerations.
    2. Use Python libraries like Beautiful Soup, Scrapy, and Selenium for web scraping.
    3. Apply data mining techniques including clustering and classification.
    4. Clean, process, and prepare data for analysis.
    5. Develop automation scripts for data extraction and analysis.
    6. Derive actionable insights from data through real-world applications.

    Prerequisites

    • Basic Python programming knowledge
    • Familiarity with HTML and web structures
    • Basic understanding of data analysis concepts

    Lab Setup

    • Python environment with Jupyter Notebook or similar
    • Required libraries: Beautiful Soup, Scrapy, Selenium, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
    • Internet access for live data scraping exercises

    Teaching Methodology

    • Interactive lectures and demonstrations
    • Hands-on coding exercises
    • Real-world case studies and examples
    • Continuous assessments through quizzes and challenges

  • (0)
  • Deep Learning with TensorFlow
    Artificial Intelligence
    Preview Course

    Artificial Intelligence

    Deep Learning with TensorFlow

    HRDC Reg. No: 10001465051
    Duration: 35 hours (5 days)


    Course Overview

    This comprehensive course provides a deep dive into deep learning concepts and their practical implementation using TensorFlow. Participants will build, train, and deploy neural networks, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The course combines theoretical foundations with hands-on labs to enable participants to solve real-world problems using deep learning techniques.


    Who Should Attend

    • Target Audience:

      • Data scientists
      • Machine learning engineers
      • Software developers
      • Researchers in AI/ML
      • Professionals upskilling in TensorFlow and deep learning
    • Target Industries:
      Information technology, healthcare, finance, automotive, retail, and any industry leveraging AI and machine learning

    Learning Outcomes

    By completing this course, participants will:

    1. Understand deep learning fundamentals and neural network architecture.
    2. Build and train various neural network models using TensorFlow.
    3. Apply deep learning to tasks such as classification, regression, image processing, and NLP.
    4. Optimize model performance using regularization, dropout, and advanced optimizers.
    5. Deploy models in production environments using TensorFlow Serving.

    Prerequisites

    • Basic knowledge of Python programming
    • Familiarity with linear algebra, calculus, and basic statistics
    • Experience with machine learning concepts (recommended)

    Lab Setup

    • Software: TensorFlow, Python (Jupyter Notebook or PyCharm), Anaconda
    • Hardware: Minimum 8GB RAM, GPU-enabled systems (preferred)
    • Tools: Docker for containerized environments (optional)

    Teaching Methodology

    • Lectures: Theoretical insights through interactive presentations
    • Hands-On Labs: Practical coding sessions in TensorFlow
    • Case Studies: Real-world examples for applied learning
    • Group Activities: Collaborative projects for problem-solving
    • Q&A Sessions: Reinforcement of concepts through discussions

  • (0)
  • Digital Image Processing
    Artificial Intelligence
    Preview Course

    Artificial Intelligence

    Digital Image Processing

    HRDC Reg. No: 10001463721
    Duration: 21 hours (3 days)


    Course Overview

    This course offers a comprehensive introduction to digital image processing, focusing on essential concepts, techniques, and applications. Participants will explore image enhancement, filtering, segmentation, feature extraction, and advanced algorithms for transformation and compression. Hands-on exercises and real-world examples will prepare participants for applications in fields such as medical imaging, video processing, and computer vision.


    Who Should Attend

    • Target Audience:

      • Software engineers and developers
      • Students and professionals in computer science and electronics
      • Researchers in digital image processing
      • Data scientists and analysts working with image data
    • Target Industries:
      Information technology, healthcare and medical imaging, robotics and automation, surveillance and security, media and entertainment

    Learning Outcomes

    By completing this course, participants will:

    1. Understand the fundamentals of digital image processing.
    2. Enhance image quality through brightness, contrast adjustments, and histogram equalization.
    3. Apply filtering techniques for noise reduction and edge detection.
    4. Implement image segmentation for object identification and analysis.
    5. Utilize advanced methods for image transformation and compression.
    6. Develop image processing applications using Python or MATLAB.

    Prerequisites

    • Basic knowledge of programming (Python or MATLAB preferred)
    • Familiarity with linear algebra and calculus
    • Understanding of signals and systems (recommended)

    Lab Setup

    • Computers with Python or MATLAB installed
    • Access to image processing libraries (OpenCV, PIL, MATLAB Image Processing Toolbox)
    • Sample image datasets for exercises

    Teaching Methodology

    • Lectures: Explaining theoretical concepts with real-world examples
    • Hands-On Labs: Practical coding sessions and exercises
    • Case Studies: Industry-specific applications of image processing
    • Quizzes and Assessments: To evaluate learning progress
    • Group Discussions: Collaborative problem-solving activities

  • (0)
  • GIS Application in Spatial Data Analysis
    Artificial Intelligence
    Preview Course

    Artificial Intelligence

    GIS Application in Spatial Data Analysis

    HRDC Reg. No: 10001464412
    Duration: 21 hours (3 days)


    Course Overview

    This course introduces participants to spatial data analysis concepts and GIS applications. Using ArcGIS Pro, participants will gain hands-on experience in spatial data processing, manipulation, and visualization. By the end of the program, they will be proficient in spatial analysis techniques and comfortable working with ArcGIS software.


    Who Should Attend

    • Target Audience:

      • GIS Analysts
      • Urban Planners
      • Environmental Scientists
      • Data Analysts
      • Surveyors
      • Engineers
      • Geographers
    • Target Industries:
      Urban planning, environmental management, utilities, infrastructure, real estate, public safety, and emergency management

    Learning Outcomes

    By completing this course, participants will:

    1. Understand spatial data types, formats, and applications.
    2. Perform spatial data processing and manipulation with ArcGIS.
    3. Apply cartographic principles and symbolization techniques for visualization.
    4. Use advanced spatial analysis methods such as clustering and forecasting.
    5. Develop automated workflows in ArcGIS.

    Prerequisites

    • Bachelor’s degree with basic knowledge of spatial data types
    • Familiarity with data charting and plotting
    • Access to a trial version of ArcGIS Pro

    Teaching Methodology

    • Hands-on practice with ArcGIS Pro
    • Interactive demonstrations and quizzes
    • Case-based learning with real-world examples
    • Guided step-by-step instructions

  • (0)
  • Image Processing in Python
    Artificial Intelligence
    Preview Course

    Artificial Intelligence

    Image Processing in Python

    HRDC Reg. No: 10001306819
    Duration: 35 hours (5 days)


    Course Overview

    This course provides an in-depth introduction to image processing using Python. It covers essential concepts, numerical data manipulation with NumPy, plotting with Matplotlib, and hands-on exercises in image processing. Participants will also implement machine learning and deep learning algorithms for advanced applications, leveraging libraries like OpenCV and Keras.


    Who Should Attend

    • Target Audience:

      • Beginners and professionals in image processing
      • Machine learning and AI enthusiasts
      • Developers aiming to implement image-based algorithms
    • Target Industries:
      IT, artificial intelligence, computer vision, healthcare imaging, and automation

    Learning Outcomes

    By completing this course, participants will:

    1. Understand the fundamentals of image processing, including concepts, pixels, and resolutions.
    2. Manipulate numerical data using NumPy.
    3. Create plots and visualizations using Matplotlib.
    4. Process images with OpenCV, including color space conversion and image transformations.
    5. Implement object detection techniques such as edge detection and template matching.
    6. Apply machine learning and deep learning algorithms to image data using Keras and neural networks.

    Teaching Methodology

    • Instructor-led interactive sessions
    • Hands-on exercises and real-world projects
    • Group discussions and case studies

  • (0)
  • Introduction to Machine Learning, Deep Learning, and Generative AI
    Artificial Intelligence
    Preview Course

    Artificial Intelligence

    Introduction to Machine Learning, Deep Learning, and Generative AI

    HRDC Registration Number: 10001462238
    Duration: 14 Hours


    Course Overview

    This training provides a comprehensive introduction to the concepts and applications of Machine Learning, Deep Learning, and Generative AI. Participants will gain both theoretical knowledge and hands-on experience through practical exercises, enabling them to effectively apply these technologies in real-world scenarios.


    Who Should Attend

    Professionals across the following industries and roles are encouraged to attend:

    Industries:

    • Technology
    • Healthcare
    • Finance
    • Retail
    • Education

    Participants:

    • Data Scientists
    • Machine Learning Engineers
    • AI Researchers
    • Software Developers
    • IT Professionals

    Why Choose This Course

    This HRDC-claimable course (Reg. No: 10001462238) equips participants with the skills to harness cutting-edge AI technologies, offering hands-on training with tools like TensorFlow, Keras, and OpenAI’s APIs.


    Learning Outcomes

    Participants will:

    • Understand key concepts and applications of Machine Learning, Deep Learning, and Generative AI.
    • Create and interpret machine learning models for regression and classification tasks.
    • Build and train neural networks using TensorFlow and Keras.
    • Gain practical knowledge of Generative AI and its applications, including ChatGPT.
    • Develop skills in effective prompting and chatbot creation using OpenAI tools.

    Prerequisites

    • Basic knowledge of Python programming.

    Lab Setup

    • Computers with Python, TensorFlow, and Keras installed.
    • Access to OpenAI API.
    • Sample datasets for regression and classification tasks.
    • Development environment for chatbot creation.

    Teaching Methodology

    • Lectures and theoretical discussions.
    • Hands-on practical exercises and labs.
    • Real-world case studies.
    • Interactive Q&A sessions.

  • (0)
  • Introduction to Python
    Artificial Intelligence
    Preview Course

    Artificial Intelligence

    Introduction to Python

    HRDC Reg. No: 10001464567
    Duration: 14 hours (2 days)


    Course Overview

    This beginner-friendly course introduces the fundamentals of Python programming, covering syntax, data types, control structures, and basic functions. Participants will learn to write Python programs and gain a solid foundation in programming principles, preparing them for more advanced topics or applications.


    Who Should Attend

    • Target Audience:

      • Programming beginners
      • Professionals adding Python to their skill set
      • Students and enthusiasts in data analysis, automation, or software development
    • Target Industries:
      IT, data science, automation, web development, and education

    Why Choose This Course:
    HRDC Claimable (Reg. No: 10001464567)


    Learning Outcomes

    By completing this course, participants will:

    1. Understand Python programming basics, including syntax and semantics.
    2. Write scripts for data manipulation and basic analysis.
    3. Use control flow structures such as loops and conditionals.
    4. Define and use functions and modules in Python.
    5. Handle errors and exceptions in Python programs.
    6. Work with basic data structures: lists, tuples, dictionaries, and sets.

    Prerequisites

    • No prior programming experience is required.
    • Basic computer literacy.

    Lab Setup

    • Python 3.x installed on participant computers
    • IDE or text editor (e.g., PyCharm, VS Code, Jupyter Notebook)
    • Internet access for package installation

    Teaching Methodology

    • Interactive lectures with live coding demonstrations
    • Hands-on exercises and coding challenges
    • Group activities and practice problems
    • Q&A sessions for troubleshooting

  • (0)
  • Machine Learning Fundamentals: Practical Application with Python
    Artificial Intelligence
    Preview Course

    Artificial Intelligence

    Machine Learning Fundamentals: Practical Application with Python

    HRDC Reg. No: 10001321038
    Duration: 14 hours (2 days)


    Course Overview

    This course provides a thorough introduction to machine learning, covering foundational concepts, algorithms, and practical applications. Participants will explore topics such as data handling, model selection, and evaluation, and gain hands-on experience in implementing machine learning algorithms using Python to solve real-world problems.


    Who Should Attend

    • Target Audience:

      • Software engineers
      • College students and research scholars
      • Enthusiasts in machine learning and Python programming
      • Individuals interested in solving real-world problems with ML
    • Target Industries:
      IT, data analytics, software development, and academia

    Why Choose This Course:
    HRDC Claimable (Reg. No: 10001321038)


    Learning Outcomes

    By completing this course, participants will:

    1. Understand the fundamental challenges in machine learning, including data management and model complexity.
    2. Develop a strong foundation in learning algorithms.
    3. Evaluate and optimize machine learning models effectively.
    4. Apply machine learning techniques to real-world problems using Python.
    5. Gain proficiency in tools such as NumPy, Pandas, Matplotlib, and Scikit-learn.

    Prerequisites

    • Basic knowledge of Python programming

    System Requirements

    • Software: Python 3.x, Anaconda 3 or newer
    • Hardware: Minimum 8GB RAM, 2GHz processor

    Teaching Methodology

    • Instructor-led sessions
    • Hands-on exercises
    • Practical projects with real-world datasets

  • (0)
  • Machine Learning Operations Fundamental
    Artificial Intelligence
    Preview Course

    Artificial Intelligence

    Machine Learning Operations Fundamental

    HRDC Reg. No: 10001411721
    Duration: 35 hours (5 days)


    Course Overview

    This course provides foundational knowledge of MLOps, focusing on deploying, operating, and managing machine learning models in production environments. Participants will learn core technologies, workflows, and best practices, including CI/CD pipelines, Kubernetes, AI platforms, and cloud infrastructure, to ensure reliable and scalable ML operations.


    Who Should Attend

    • Target Audience:

      • Data scientists
      • Data engineers and analysts
      • ML engineers
      • DevOps engineers
      • MLOps enthusiasts and professionals
      • Machine learning professionals interested in production deployment
    • Target Industries:
      IT, data analytics, artificial intelligence, and cloud computing

    Learning Outcomes

    By completing this course, participants will:

    1. Understand and apply core technologies for MLOps.
    2. Configure cloud-based architectures (AWS, Azure, GCP) for ML environments.
    3. Implement reproducible training and inference workflows.
    4. Integrate CI/CD practices for ML systems.
    5. Manage and monitor deployed ML models effectively.

    Prerequisites

    • Completion of "Machine Learning with Google Cloud" or equivalent experience

    Teaching Methodology

    • Instructor-led sessions
    • Hands-on exercises and projects
    • Practical implementation of real-world scenarios

  • (0)
  • Python Object-Oriented Programming (OOP) and Advanced Techniques
    Artificial Intelligence
    Preview Course

    Artificial Intelligence

    Python Object-Oriented Programming (OOP) and Advanced Techniques

    HRDC Reg. No: 10001321147
    Duration: 14 hours (2 days)


    Course Overview

    This course offers a comprehensive exploration of Object-Oriented Programming (OOP) in Python alongside advanced techniques for data handling, file manipulation, and database operations. Participants will learn to implement OOP principles, utilize inheritance for reusable code, and work with advanced Python capabilities like JSON, regular expressions, and CRUD operations with databases.


    Who Should Attend

    • Target Audience:

      • Students aspiring to enter the IT industry
      • Software developers enhancing Python programming skills
      • DevOps engineers expanding technical expertise
      • Database administrators integrating Python for operations
      • System administrators leveraging Python for management
      • Test engineers enhancing testing capabilities
    • Target Industries:
      IT, software development, database management, and system administration

    Learning Outcomes

    By completing this course, participants will:

    1. Understand OOP principles and their implementation in Python.
    2. Differentiate and apply instance and class attributes.
    3. Reuse code with inheritance to improve design and reduce repetition.
    4. Handle patterns in input files and convert data structures to JSON.
    5. Perform database connections and CRUD operations.
    6. Apply advanced techniques like regular expressions and logging.
    7. Prepare for roles such as software developers, DevOps engineers, and database administrators.

    Prerequisites

    • Foundational knowledge of Python programming

    System Requirements

    • Operating System: Compatible with Windows, Linux, or macOS
    • Python Version: Python 3.x or later
    • Hardware: Minimum 4GB RAM (8GB recommended), 2GHz processor

    Teaching Methodology

    • Instructor-led sessions
    • Hands-on exercises and real-life case studies
    • Small projects to reinforce learning

  • (0)