Course Details

Unlock the Power of Data: Machine Learning Fundamentals Training Course

INTRODUCTION

Machine Learning (ML) is revolutionizing industries by enabling computers to learn from data without explicit programming. This Machine Learning Fundamentals Training Course provides a comprehensive introduction to the core concepts and techniques of ML, empowering participants to understand and apply these powerful tools. Whether you're a beginner or looking to solidify your foundational knowledge, this course will equip you with the skills to harness the potential of ML for data-driven insights and problem-solving.

This course is tailored for data analysts, software developers, business professionals, and anyone interested in gaining a practical understanding of machine learning. Through a blend of theoretical lectures, hands-on coding exercises, and real-world case studies, attendees will learn to build and evaluate ML models, understand key algorithms, and apply ML techniques to solve practical problems. The course emphasizes the importance of data preparation, model evaluation, and ethical considerations in machine learning.

DURATION

5 days

COURSE OBJECTIVES

Upon completion of this course, participants will be able to:

• Understand the fundamental concepts and terminology of machine learning.
• Differentiate between supervised, unsupervised, and reinforcement learning.
• Implement and evaluate common machine learning algorithms.
• Prepare and preprocess data for machine learning tasks.
• Build and train machine learning models using Python and relevant libraries.
• Evaluate model performance and select appropriate metrics.
• Apply machine learning techniques to solve real-world problems.
• Understand the ethical considerations in machine learning.

COURSE MODULES

• Introduction to Machine Learning:
• Defining machine learning and its applications.
• Understanding the history and evolution of machine learning.
• Exploring the different types of machine learning (supervised, unsupervised, reinforcement).
• Understanding the machine learning workflow.
• Data Preparation and Preprocessing:
• Understanding the importance of data quality.
• Techniques for data cleaning, transformation, and feature engineering.
• Handling missing data and outliers.
• Data splitting and cross-validation.
• Supervised Learning: Regression:
• Understanding linear regression and its variations.
• Implementing regression models using Python libraries (e.g., scikit-learn).
• Evaluating regression model performance (e.g., MSE, R-squared).
• Understanding polynomial regression.
• Supervised Learning: Classification:
• Understanding classification algorithms (e.g., logistic regression, decision trees, support vector machines).
• Implementing classification models using Python libraries.
• Evaluating classification model performance (e.g., accuracy, precision, recall, F1-score).
• Understanding how to handle imbalanced datasets.
• Unsupervised Learning: Clustering:
• Understanding clustering algorithms (e.g., k-means, hierarchical clustering).
• Implementing clustering models using Python libraries.
• Evaluating clustering performance (e.g., silhouette score).
• Understanding dimensionality reduction techniques.
• Model Evaluation and Selection:
• Understanding different model evaluation metrics.
• Techniques for model selection and hyperparameter tuning.
• Understanding the bias-variance trade-off.
• Understanding the importance of cross validation.
• Introduction to Neural Networks and Deep Learning:
• Understanding the basics of neural networks.
• Exploring deep learning architectures (e.g., feedforward neural networks).
• Implementing simple neural networks using Python libraries (e.g., TensorFlow, Keras).
• Understanding the difference between machine learning and deep learning.
• Ethical Considerations and Real-World Applications:
• Understanding the ethical implications of machine learning.
• Addressing bias and fairness in machine learning models.
• Exploring real-world applications of machine learning across various industries.
• Understanding the future of machine learning.

CERTIFICATION

• Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

• Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

• Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

• Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com