Supervised vs. Unsupervised Learning: The Data Scientist's Toolkit Training Course

Supervised and unsupervised learning are the two foundational pillars of machine learning, each offering a distinct approach to solving complex data problems. This course demystifies these core concepts, providing a comprehensive guide to when and how to apply each technique. You’ll learn how to build predictive models using labeled data (supervised learning) and uncover hidden patterns and structures in unlabeled data (unsupervised learning), a crucial skill for any modern data professional.

This training program goes beyond theory, offering a hands-on, practical journey through the most important algorithms in both fields. From building a spam classifier to segmenting customer data, you’ll gain the skills to tackle a wide range of real-world challenges. By the end of this course, you'll not only understand the differences but also be proficient in choosing the right methodology and implementing powerful machine learning solutions.

Duration

5 days

Target Audience

This course is designed for aspiring data scientists, data analysts, machine learning engineers, and software developers who want a deep, practical understanding of the core concepts of supervised and unsupervised learning.

Course Objectives

  • Differentiate clearly between supervised and unsupervised learning paradigms.
  • Master key supervised learning algorithms for both regression and classification.
  • Learn to effectively preprocess and prepare data for both learning types.
  • Gain proficiency in implementing and evaluating unsupervised learning models.
  • Understand the concepts of model validation and performance metrics.
  • Acquire hands-on experience with popular Python libraries like scikit-learn.
  • Identify and address common challenges such as overfitting and underfitting.
  • Learn to choose the correct algorithm for a given data problem.
  • Explore real-world applications of both supervised and unsupervised learning.
  • Develop a systematic workflow for building a complete machine learning pipeline.

Course Modules

Module 1: The Foundations of Machine Learning

  • Introduction to the machine learning landscape.
  • Defining supervised learning and its applications.
  • Defining unsupervised learning and its applications.
  • A brief overview of semi-supervised and reinforcement learning.
  • Understanding the machine learning workflow.

Module 2: Supervised Learning: Regression

  • Introduction to regression and its use cases.
  • Linear regression: the basics and how to interpret results.
  • Multiple linear regression and polynomial regression.
  • Evaluating regression models with metrics like MAE and RMSE.
  • Practical lab: predicting continuous values from a dataset.

Module 3: Supervised Learning: Classification

  • Introduction to classification and its business impact.
  • Logistic regression for binary classification.
  • Decision trees and random forests for complex datasets.
  • Understanding the confusion matrix and classification metrics (precision, recall).
  • Practical lab: building a model to classify customer churn.

Module 4: Data Preprocessing for Supervised Learning

  • Handling missing data and data imputation techniques.
  • Encoding categorical variables (one-hot, label encoding).
  • Feature scaling and normalization for different algorithms.
  • Splitting data into training, validation, and test sets.
  • An introduction to feature engineering.

Module 5: Unsupervised Learning: Clustering

  • Introduction to unsupervised learning's goal of finding structure.
  • K-Means clustering: a hands-on guide to implementation.
  • Determining the optimal number of clusters using the Elbow Method.
  • Hierarchical clustering and its use in data exploration.
  • Practical lab: segmenting a customer base for targeted marketing.

Module 6: Unsupervised Learning: Dimensionality Reduction

  • The problem of the "curse of dimensionality."
  • Principal Component Analysis (PCA) for feature extraction.
  • Using PCA for data visualization and simplifying models.
  • Introduction to t-SNE for visualizing complex data.
  • Practical lab: reducing a high-dimensional dataset for easier analysis.

Module 7: Unsupervised Learning: Association Rule Mining

  • Discovering relationships between variables in a dataset.
  • The Apriori algorithm and its application in market basket analysis.
  • Understanding support, confidence, and lift.
  • Case study: a retail example of product recommendation.
  • Practical lab: finding hidden item associations in a transaction dataset.

Module 8: Model Evaluation & Cross-Validation

  • The importance of validating models and avoiding overfitting.
  • Understanding the bias-variance trade-off.
  • K-fold cross-validation and its benefits.
  • Choosing the right performance metric for your problem.
  • Troubleshooting a model that is not performing well.

Module 9: The Right Tool for the Job

  • Developing a systematic approach to problem-solving.
  • Mapping business problems to machine learning tasks.
  • Decision-making frameworks for choosing between supervised and unsupervised methods.
  • Case studies of how professionals choose their approach.
  • Group exercise: solving new problems by selecting the appropriate algorithm.

Module 10: Advanced Supervised Learning

  • An introduction to more advanced classification algorithms.
  • Support Vector Machines (SVMs) and kernel tricks.
  • An overview of Gradient Boosting Machines (GBM).
  • Introduction to neural networks for classification.
  • Practical lab: building an advanced classifier with an ensemble method.

Module 11: Introduction to Semi-Supervised Learning

  • The hybrid approach: using both labeled and unlabeled data.
  • When to use semi-supervised learning.
  • Techniques like self-training and label propagation.
  • Practical examples from fields with limited labeled data.
  • Exploring the future of this hybrid approach.

Module 12: The Machine Learning Career Path

  • Best practices for organizing and presenting your projects.
  • Building a portfolio to showcase your skills.
  • Essential tools and technologies for data scientists.
  • Navigating the job market and interview process.
  • Q&A with a focus on career development.

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 is provided by the institute. 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

For More Details call: +254-114-087-180

 

Supervised Vs. Unsupervised Learning: The Data Scientist's Toolkit Training Course in Namibia
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