Machine Learning

Machine Learning

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Duration

3 Days

Certified Instructor

Course Id

nextec1201

Course Description

Course Overview

Machine learning is a subfield of Artificial Intelligence that focuses on enabling machines to learn from their environment autonomously, without external assistance or intervention. The Machine Learning course offers a comprehensive introduction to the principles and techniques of machine learning, enabling students to build predictive models and extract insights from data. The course covers a wide range of topics, including supervised and unsupervised learning, model evaluation, feature selection, and model optimization. Students will learn how to apply machine learning algorithms to real-world datasets and gain hands-on experience in developing machine learning solutions. Through this machine learning course, learners will gain the skills and knowledge necessary to navigate this fascinating field. They will explore the principles, techniques, and tools used in machine learning and discover how to effectively apply them. 

Prerequisites

  • Basic programming skills (preferably in Python)
  • Familiarity with data structures and algorithms
  • Understanding of probability and statistics
  • Knowledge of linear algebra and calculus
  • Some exposure to computer science concepts and algorithms

Methodology

The course adopts a blended learning approach, combining theoretical lectures, practical coding exercises, and hands-on labs. The lectures provide a solid understanding of machine learning principles, algorithms, and techniques. The practical coding exercises help students implement and apply machine learning algorithms to various datasets. The labs offer opportunities to work on larger projects and gain practical experience in building end-to-end machine learning solutions.

Course Outline

Introduction to Machine Learning

Overview of machine learning concepts and applications

Types of machine learning algorithms

The machine learning workflow

Supervised Learning

Linear regression

Logistic regression

Decision trees and ensemble methods (e.g., random forests, gradient boosting)

Model Evaluation and Selection

Training set, validation set, and test set

Evaluation metrics (e.g., accuracy, precision, recall, F1-score)

Cross-validation and hyperparameter tuning

Unsupervised Learning

Clustering algorithms (e.g., k-means, hierarchical clustering)

Dimensionality reduction techniques (e.g., principal component analysis, t-SNE)

Feature Engineering and Selection

Handling missing data

Feature scaling and normalization

Feature selection techniques (e.g., forward selection, backward elimination)

Neural Networks and Deep Learning

Introduction to artificial neural networks

Feedforward neural networks and backpropagation

Convolutional neural networks and recurrent neural networks

Model Deployment and Interpretation

Model deployment options (e.g., web services, APIs)

Model interpretation and explainability techniques

Monitoring and updating deployed models

Advanced Topics in Machine Learning

Support Vector Machines (SVM)

Reinforcement Learning

Generative Adversarial Networks (GANs)

Outcome

Upon completing the course, students will:

  • Understand the fundamental concepts, algorithms, and techniques of machine learning.
  • Be able to apply supervised and unsupervised learning algorithms to solve real-world problems.
  • Gain proficiency in popular machine learning libraries and frameworks (e.g., scikit-learn, TensorFlow).
  • Evaluate and select appropriate models based on evaluation metrics and validation techniques.
  • Perform feature engineering and selection to improve model performance.
  • Have hands-on experience in developing and deploying machine learning solutions.

Labs

The course includes hands-on labs to reinforce the theoretical concepts and provide practical experience. The labs may include:

  • Implementing linear regression for predicting housing prices based on a dataset.
  • Building a decision tree classifier for a classification task (e.g., spam detection) using scikit-learn.
  • Applying clustering algorithms (e.g., k-means) to group similar data points in a dataset.
  • Developing a convolutional neural network (CNN) for image classification on a dataset (e.g., MNIST).
  • Performing feature selection and engineering techniques on a dataset to improve model performance.
  • Deploying a trained machine learning model as a web service using Flask or other frameworks.

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