Course Details

Deep Learning with TensorFlow & PyTorch Training Course

INTRODUCTION

Machine Learning (ML) is rapidly transforming industries, enabling computers to learn from data and make predictions or decisions without explicit programming. This "Machine Learning Fundamentals" training course provides a solid foundation in the core concepts and techniques of ML, empowering participants to understand and apply these powerful tools. We'll demystify ML, moving beyond complex jargon to provide practical, hands-on experience. Participants will learn to build, evaluate, and deploy ML models, enabling them to leverage data for informed decision-making and problem-solving.

DURATION

5 days

TARGET AUDIENCE

This course is designed for:

• Data analysts and scientists who want to expand their ML skills.
• Software developers and engineers interested in integrating ML into applications.
• Business professionals who want to understand and apply ML for business insights.
• Students and researchers seeking a foundational understanding of ML.
• Anyone with a basic understanding of programming and data who wants to learn ML.

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 using Python.
• Prepare and preprocess data for machine learning tasks.
• Build and train machine learning models using libraries like scikit-learn.
• Evaluate model performance using appropriate metrics.
• Apply machine learning techniques to solve real-world problems.
• Understand the ethical considerations and limitations of machine learning.

COURSE MODULES

• Introduction to Machine Learning:
• What is machine learning?
• Types of machine learning: supervised, unsupervised, and reinforcement learning.
• Applications of machine learning in various industries.
• The machine learning workflow: data collection, preprocessing, model training, evaluation, and deployment.
• Data Preprocessing and Preparation:
• Data cleaning: handling missing values, outliers, and inconsistencies.
• Feature engineering: creating and transforming features for better model performance.
• Data scaling and normalization.
• Splitting data into training, validation, and test sets.
• Supervised Learning: Regression:
• Linear regression: simple and multiple linear regression.
• Polynomial regression.
• Evaluating regression models: mean squared error (MSE), R-squared.
• Implementation using scikit-learn.
• Supervised Learning: Classification:
• Logistic regression.
• Decision trees and random forests.
• Support vector machines (SVMs).
• Evaluating classification models: accuracy, precision, recall, F1-score, ROC curves.
• Handling imbalanced datasets.
• Unsupervised Learning: Clustering:
• K-means clustering.
• Hierarchical clustering.
• Evaluating clustering performance: silhouette score.
• Applications of clustering.
• Model Evaluation and Selection:
• Cross-validation techniques.
• Hyperparameter tuning: grid search, random search.
• Bias-variance trade-off.
• Model selection criteria.
• Introduction to Neural Networks:
• Basic concepts of neural networks: neurons, layers, activation functions.
• Feedforward neural networks.
• Introduction to deep learning.
• Using Keras or Tensorflow for simple neural networks.
• Ethical Considerations and Real-World Applications:
• Bias and fairness in machine learning.
• Privacy and security in machine learning.
• Responsible AI practices.
• Case studies and real-world applications of machine learning.
• Limitations of ML.

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