Machine Learning Essentials

Duration: 6 Weeks (2 Days per Week)

Week 1: Introduction to Machine Learning

Day 1: Machine Learning Fundamentals

  • What is Machine Learning?

  • Types of Machine Learning (Supervised, Unsupervised, Reinforcement)

  • Real-world Machine Learning Applications

  • Machine Learning Workflow

Week 2: Supervised Learning

Day 2: Understanding Supervised Learning

  • Linear Regression

  • Simple Linear Regression

  • Multiple Linear Regression

  • Assumptions and Model Interpretation

Day 3: Logistic Regression

  • Binary Logistic Regression

  • Multinomial Logistic Regression

  • Model Evaluation for Classification

Week 3: Ensemble Methods

Day 4: Introduction to Ensemble Learning

  • Decision Trees

  • Random Forest

  • Bagging and Feature Importance

Day 5: Gradient Boosting

  • Adaboost

  • Gradient Boosting Machines (GBM)

  • XGBoost and LightGBM

  • Hyperparameter Tuning for Ensemble Models

Week 4: Model Evaluation and Validation

Day 6: Training, Validation, and Test Sets

  • Cross-Validation Techniques

  • K-Fold Cross-Validation

  • Leave-One-Out Cross-Validation

  • Performance Metrics for Regression (MAE, MSE, R2)

Day 7: Performance Metrics for Classification

  • Accuracy, Precision, Recall, F1-Score

  • Confusion Matrix

  • Overfitting and Underfitting

Week 5: Unsupervised Learning

Day 8: Understanding Unsupervised Learning

  • Clustering Concepts

  • K-Means Clustering

  • Algorithm Steps

  • Choosing the Number of Clusters (K)

Day 9: Evaluation and Other Clustering Algorithms

  • Evaluation of Clustering Results

  • Hierarchical Clustering

  • DBSCAN and Other Clustering Algorithms

Week 6: Model Deployment and Beyond

Day 10: Introduction to Model Deployment

  • What is Model Deployment?

  • Deployment Options (On-Premise, Cloud, Edge)

  • Common Deployment Platforms (Docker, Kubernetes)

  • Model Serving Frameworks (TensorFlow Serving, PyTorch Serve)

Day 11: Future Learning Paths and Ethics

  • Advanced Machine Learning Topics (Deep Learning, Reinforcement Learning)

  • Natural Language Processing (NLP) and Computer Vision

  • Feature Engineering and Feature Selection

  • Ethical Considerations in Machine Learning

Day 12: Capstone Project (Hands-On)

  • Participants work on a supervised or unsupervised learning project.

  • Data preparation, model building, evaluation, and deployment (if possible).

  • Presentation of project results and lessons learned.

Welcome to the world of Machine Learning Essentials! In an era defined by data-driven decision-making, machine learning has emerged as a pivotal technology that empowers businesses, researchers, and individuals to unlock the hidden insights within their data.

Our comprehensive "Machine Learning Essentials" course is designed to provide you with a solid foundation in machine learning principles and techniques. Whether you're a complete novice or looking to deepen your understanding of the field, this course will equip you with the knowledge and skills needed to embark on your journey into the exciting realm of machine learning.

Contact us to book this course

AITechs