Course Details

Machine Learning Fundamentals Training: Building Predictive Models and Gaining Data Insights

INTRODUCTION

Machine Learning (ML) is revolutionizing how we interact with technology and solve complex problems. This Machine Learning Fundamentals training course provides a comprehensive introduction to the core concepts and techniques of ML, equipping participants with the skills to build predictive models, gain insights from data, and apply ML to real-world scenarios. Participants will learn about supervised, unsupervised, and reinforcement learning, explore popular ML algorithms, and gain hands-on experience through practical exercises. This ML foundations training is perfect for anyone seeking to enter the exciting field of data science and artificial intelligence. This course covers key areas like supervised learning, unsupervised learning, model evaluation, and feature engineering, empowering you with a solid foundation in machine learning.

DURATION

5 days

TARGET AUDIENCE

This course is designed for a broad audience, including:

• Data Analysts: Professionals seeking to enhance their data analysis skills with ML techniques.
• Business Analysts: Individuals looking to leverage ML for business insights and decision-making.
• Software Developers: Those wanting to incorporate ML capabilities into their applications.
• Students: Individuals exploring career paths in data science and AI.
• Anyone curious about Machine Learning: Individuals wanting to gain a basic understanding of ML concepts and applications.

COURSE OBJECTIVES

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

• Define Machine Learning and its various types (supervised, unsupervised, reinforcement).
• Understand core ML concepts (algorithms, models, training data, evaluation metrics).
• Implement common ML algorithms (linear regression, classification, clustering).
• Evaluate model performance and select the best model for a given task.
• Preprocess and prepare data for ML models.
• Apply ML techniques to solve real-world problems.
• Understand the limitations and potential of ML.
• Communicate effectively about ML concepts and findings.

COURSE MODULES

• Introduction to Machine Learning:

• Defining ML and its relationship to AI.
• Exploring the different types of ML (supervised, unsupervised, reinforcement).
• Understanding the ML workflow (data collection, preprocessing, model training, evaluation).
• Discussing the applications of ML in various industries.

• Supervised Learning - Regression:

• Introduction to regression problems and algorithms.
• Implementing linear regression and polynomial regression.
• Evaluating regression models using metrics like mean squared error (MSE) and R-squared.
• Hands-on exercises with real-world datasets.

• Supervised Learning - Classification:

• Introduction to classification problems and algorithms.
• Implementing logistic regression, decision trees, and support vector machines (SVMs).
• Evaluating classification models using metrics like accuracy, precision, recall, and F1-score.
• Hands-on exercises with real-world datasets.

• Unsupervised Learning - Clustering:

• Introduction to clustering problems and algorithms.
• Implementing k-means clustering and hierarchical clustering.
• Evaluating clustering performance using metrics like silhouette score.
• Hands-on exercises with real-world datasets.

• Model Evaluation and Selection:

• Understanding the importance of model evaluation.
• Techniques for splitting data into training, validation, and test sets.
• Cross-validation and hyperparameter tuning.
• Model selection criteria and best practices.

• Feature Engineering and Data Preprocessing:

• Importance of data preprocessing for ML models.
• Techniques for data cleaning, transformation, and feature engineering.
• Handling missing values and categorical data.
• Feature scaling and normalization.

• Introduction to Deep Learning (Optional):

• Basic concepts of neural networks and deep learning.
• Exploring different types of neural networks (CNNs, RNNs).
• Introduction to deep learning frameworks (TensorFlow, PyTorch).
• High-level overview of deep learning applications.

• Applying Machine Learning to Real-World Problems:

• Case studies of ML applications in various domains.
• Best practices for deploying ML models in production.
• Ethical considerations in machine learning.
• The future of machine learning and its potential impact.

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