Machine Learning

Intermediate Machine Learning

This course is designed for those who have a basic understanding of machine learning and want to deepen their knowledge. Participants will explore more advanced algorithms, model tuning techniques, and data handling strategies. The course covers topics like ensemble methods, support vector machines, natural language processing, and deep learning basics. With practical exercises and real-world applications, learners will enhance their ability to build more accurate and complex machine learning models.

  • 5/5.0
  • 14000 Students
  • Beginner
  • English
Course Description

This course is designed for those who have a basic understanding of machine learning and want to deepen their knowledge. Participants will explore more advanced algorithms, model tuning techniques, and data handling strategies. The course covers topics like ensemble methods, support vector machines, natural language processing, and deep learning basics. With practical exercises and real-world applications, learners will enhance their ability to build more accurate and complex machine learning models.

What you’ll learn
  • Comprehensive Learning Path: Step-by-step guide to mastering topics.
  • Interactive Learning: Engage with quizzes, discussions, and activities.
  • Skill Development: Improve technical and soft skills.
  • Flexible Learning: Learn at your own pace.
  • Career Advancement: Equip yourself for job market success.
  • Networking Opportunities: Connect with professionals and experts.
  • Practical Tools and Resources: Access industry-standard tools and resources.
  • Ongoing Support: Get continuous feedback and assistance.
  • Hands-On Projects: Complete real-world projects and tasks.
  • Final Certification: Earn a recognized course certification.

Each course is designed with the highest quality standards to ensure you gain valuable knowledge and practical skills. Whether you're exploring new areas or deepening your expertise, our courses offer a comprehensive learning experience. With detailed explanations, real-world examples, and expert guidance, you'll be equipped to apply what you've learned immediately. Join us and unlock new opportunities for growth and success

Introduction to ensemble methods: Bagging, boosting, and stacking

Decision trees, random forests, and gradient boosting machines (GBM)

Support vector machines (SVM) and kernel tricks

Importance of hyperparameters in machine learning models

Techniques for tuning hyperparameters: Grid search, random search, and Bayesian optimization

Cross-validation and model selection

Techniques for creating meaningful features: Scaling, encoding, and transformations

Methods for feature selection: Recursive feature elimination (RFE), L1 regularization

Handling categorical and numerical features

Understanding bagging and boosting algorithms (e.g., XGBoost, AdaBoost, LightGBM)

Combining multiple models for better performance

Stacking models to create a stronger prediction pipeline

Basic concepts of NLP: Tokenization, stemming, and lemmatization

Using TF-IDF for text representation

Text classification and sentiment analysis with machine learning models

Introduction to neural networks: Perceptrons, activation functions, and layers

Training deep neural networks with backpropagation

Introduction to frameworks like TensorFlow and Keras

Techniques for handling imbalanced datasets: Resampling, SMOTE, and cost-sensitive learning

Strategies for dealing with missing data: Imputation and handling outliers

Evaluation techniques for imbalanced data

Advanced evaluation metrics: ROC-AUC, precision-recall curve, F1 score

Techniques to improve model performance: Ensemble methods, model ensembling, and cross-validation

Identifying overfitting and underfitting: Regularization and bias-variance tradeoff

Case studies and examples of machine learning in various industries (finance, healthcare, marketing)

Building end-to-end machine learning pipelines

Best practices for deploying and maintaining models in production

Introduction to more advanced topics: Reinforcement learning, deep learning, and transfer learning

Tools and resources for further learning: Books, courses, and communities

Building a machine learning portfolio and preparing for real-world challenges

instructor-image

Amar Verma

Hii ama

About Instructor

Hii ama

Inspiring Learning, One Step at a Time As an educator, your dedication transforms students' lives. Our platform is designed to empower you with tools, resources, and a community that values your expertise. Share your knowledge, inspire curiosity, and help learners achieve their dreams. Together, we build a brighter future, one lesson at a time.

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This course includes

  • Lectures 40 Classes
  • Duration Hours
  • Skills Beginner
  • Language English
  • Certificate Yes

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