Advanced Machine Learning

The Advanced Machine Learning (ML) course is designed to provide experience individuals in the field of machine learning with an in-depth understanding of advanced concepts, techniques, and applications. This course is tailored for professionals, researchers, and enthusiasts who have a solid foundation in basic machine learning principles and wish to delve deeper into cutting-edge methodologies.

Starting From: 22nd January 2024

Course Duration: 20 Hours

Class Timings: 6:50PM to 9:20PM

Course Fee: ₹20,000/-


About Course

Course Objectives:

  • Reinforcement Learning:

    • Understanding advanced reinforcement learning algorithms.
    • Deep reinforcement learning techniques and applications.
    • Hands-on projects to implement reinforcement learning in real-world scenarios.

  • Deep Learning Architectures:

    • Exploration of advanced neural network architectures.
    • Transfer learning and fine-tuning for specific tasks.
    • Architectures like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).

  • Natural Language Processing (NLP):

    • Advanced NLP techniques for text analysis and understanding.
    • Sentiment analysis, text summarization, and language translation.
    • Implementation of NLP models for real-world applications.

  • Time Series Analysis:

    • Advanced methods for analyzing time series data.
    • Forecasting techniques and predictive modeling.
    • Applications in finance, healthcare, and other industries.

  • Ensemble Learning:

    • Understanding ensemble methods like bagging and boosting.
    • Model stacking and combining diverse models for improved performance.
    • Case studies showcasing the effectiveness of ensemble learning.

  • Explainable AI (XAI):

    • Techniques for making machine learning models more interpretable.
    • Model interpretability and its importance in real-world applications.
    • Ethical considerations and responsible AI practices.

  • AutoML and Hyperparameter Tuning:

    • Introduction to Automated Machine Learning (AutoML) tools.
    • Hyperparameter optimization for improving model performance.
    • Practical exercises on implementing AutoML techniques.

  • Advanced Optimization Techniques:

    • Optimization algorithms for training deep learning models.
    • Stochastic gradient descent variants and their applications.
    • Hyperparameter tuning for optimizing model convergence.

  • Edge and Federated Learning:

    • Deploying machine learning models on edge devices.
    • Federated learning and its applications in privacy-preserving scenarios.
    • Challenges and solutions in edge and federated learning.

  • Practical Applications and Capstone Project:

    • Real-world case studies highlighting the application of advanced ML techniques.
    • A capstone project where participants apply their knowledge to solve a complex problem.

Course Format:

The "Advanced Machine Learning" course employs a combination of lectures, hands-on coding exercises, and projects to reinforce learning. Participants are encouraged to actively engage with the material and seek assistance through Q&A sessions and forums.

By the end of the Advanced ML course, participants will not only have a comprehensive understanding of state-of-the-art machine learning techniques but also gain practical experience through hands-on projects and applications in various domains. This course equips individuals with the skills required to tackle complex challenges and contribute to the advancement of the field.


Earn a Certificate upon completion

Online Classes

Start instantly and learn at your own

Life Time Accessibility

Set and maintain flexible deadlines.

Intermediate Level

Understanding of basics of Machine Learning required.

Program Curriculum
  • Recommender Systems
  • RS in Python
  • RS Project
  • Natural Language Processing
  • NLP in Python
  • Deep Learning Introduction
  • Introduction to ANN
Advanced Machine Learning
  • Introduction to Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Evaluation Metrics in Machine Learning
  • Time Series Analysis
  • Big Data and Spark
  • Ethics and Bias in Data Science

Our Program Focuses On Three Key Areas

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Number 2


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