Machine Learning (ML) Course in Thanjavur (Online & Offline)

About Course

This Machine Learning Course provides students with the knowledge and skills to build systems that learn from data and make predictions. ML is the backbone of modern AI applications like speech recognition, recommendation systems, fraud detection, and predictive analytics. This course covers both theory and practical implementation using tools such as Python, NumPy, Pandas, and Scikit-Learn. Suitable for Computer Science, AI, Data Science, and IT students, as well as professionals aiming for careers in data-driven industries. Training is available in both online and offline modes, with flexible durations (Short term – 1 month / Long term – 3–6 months). A free course certificate will be awarded upon completion.

machinelearning

 Course Duration Options

  • Short-Term Training (1 Month): Covers ML fundamentals, data preprocessing, and simple regression/classification models. Includes a mini ML project.
  • Long-Term Training (3–6 Months): Advanced ML algorithms, deep learning basics, and deployment of ML models. Ends with a major project.

    Offline

    No.9 Selvam Nagar, M.C.Road, Thanjavur – 613007

    Online

    Zoom/Google Meet Sessions (Link after registration)

    Government Approved Certifications

    At Prasartech Projects & Solutions, we provide centrally approved vocational training certifications along with academic project training. Students completing these programs will receive a valid government certificate that enhances their employability and academic recognition.

    • BSSVE.IN Government approved vocational certificate (Bharat Sevak Samaj)
    • Promoted by Government of India (National Development Agency ) 
    • Reg No: TN/20029 (Authorised Training Centre)
    • Examination + Marksheet + Certificate
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What Will You Learn?

  • Fundamentals of Machine Learning & AI concepts
  • Supervised learning (regression & classification)
  • Unsupervised learning (clustering & dimensionality reduction)
  • Data preprocessing and feature engineering
  • Model training, evaluation, and optimization
  • Ensemble methods (Random Forest, Gradient Boosting)
  • Neural network basics and intro to deep learning
  • ML tools: Python, NumPy, Pandas, Scikit-Learn
  • Real-world ML projects (finance, healthcare, IoT, e-commerce)
  • Mini and major academic/industry projects

Course Content

Introduction to Machine Learning

  • What is ML? Difference between AI, ML, and DL
  • Applications of ML in real-world scenarios
  • Types of ML: supervised, unsupervised, reinforcement
  • ML workflow and lifecycle

Data Handling & Preprocessing

Supervised Learning

Unsupervised Learning & Advanced Models

Neural Networks & Projects