Master of Algorithms: An Introduction to Machine Learning Training Course

Machine learning is at the heart of the most innovative technologies today, from self-driving cars to personalized recommendation systems. This training course provides a solid foundation in the core algorithms and concepts that drive machine learning. You will gain a clear understanding of the different types of learning—supervised, unsupervised, and reinforcement—and discover how to apply foundational algorithms like linear regression, k-means clustering, and decision trees to solve real-world problems.

Designed for aspiring data scientists and developers, this program focuses on building a strong practical skillset. You'll learn to prepare data, train models, and evaluate performance using popular programming libraries. This course demystifies the complex world of machine learning, making it accessible to those with a foundational understanding of programming and mathematics, and setting you on a path to a career in AI and data science.

Duration

5 days

Target Audience

This course is ideal for software developers, data analysts, students, and anyone with a background in programming and a basic understanding of mathematics who wants to build a career in data science or artificial intelligence.

Course Objectives

  • Understand the fundamental principles of machine learning and its sub-fields.
  • Differentiate between supervised, unsupervised, and reinforcement learning.
  • Learn to prepare, clean, and preprocess data for machine learning models.
  • Master the theory and practical application of key regression and classification algorithms.
  • Gain proficiency in implementing clustering algorithms for data segmentation.
  • Understand the concepts of model evaluation and validation.
  • Learn to use popular Python libraries such as scikit-learn, NumPy, and Pandas.
  • Recognize and address common issues like overfitting and underfitting.
  • Develop a systematic approach to selecting the right algorithm for a given problem.
  • Build a portfolio of machine learning projects from scratch.

Course Modules

Module 1: Foundational Concepts

  • What is machine learning? AI, ML, and deep learning defined.
  • The machine learning workflow: from data collection to deployment.
  • Types of learning: supervised, unsupervised, and reinforcement.
  • Understanding common use cases and applications.
  • Introduction to key programming libraries for ML.

Module 2: Supervised Learning - Regression

  • Introduction to regression analysis and its purpose.
  • Linear regression: theory, implementation, and evaluation.
  • Understanding the concepts of cost function and gradient descent.
  • Polynomial regression and its applications.
  • Practical lab: predicting house prices with linear regression.

Module 3: Supervised Learning - Classification

  • Introduction to classification and its business applications.
  • Logistic regression for binary classification.
  • Decision trees and random forests: understanding ensemble methods.
  • Support Vector Machines (SVMs) and their use in complex datasets.
  • Practical lab: building a spam email classifier.

Module 4: Unsupervised Learning - Clustering

  • Exploring unsupervised learning and its goals.
  • K-Means clustering: a hands-on approach to implementation.
  • Hierarchical clustering and its use in data exploration.
  • Understanding the challenges of choosing the right number of clusters.
  • Practical lab: customer segmentation for a marketing campaign.

Module 5: Data Preprocessing

  • Handling missing values and data imputation techniques.
  • Feature scaling and normalization.
  • Encoding categorical data (one-hot encoding, label encoding).
  • Dealing with outliers and noisy data.
  • Introduction to feature engineering.

Module 6: Model Evaluation and Validation

  • Key performance metrics for regression (MAE, MSE, RMSE).
  • Key performance metrics for classification (accuracy, precision, recall, F1-score).
  • Understanding the confusion matrix.
  • Cross-validation techniques (k-fold, stratified).
  • Diagnosing common problems: bias vs. variance trade-off.

Module 7: Dimensionality Reduction

  • The curse of dimensionality and its impact on models.
  • Principal Component Analysis (PCA) for feature extraction.
  • Understanding the purpose of dimensionality reduction.
  • When and how to use techniques like t-SNE.
  • Practical lab: visualizing high-dimensional data.

Module 8: Introduction to Ensemble Methods

  • Why use ensemble learning? The wisdom of the crowd.
  • Bagging: Random Forests explained in detail.
  • Boosting: Gradient Boosting Machines (GBM) and XGBoost.
  • Stacking and its benefits.
  • Choosing the right ensemble method for your problem.

Module 9: Reinforcement Learning

  • An introduction to the RL framework: agent, environment, reward.
  • The difference between RL and other learning types.
  • Key algorithms like Q-learning and SARSA.
  • Common applications of RL, such as game playing and robotics.
  • Basic hands-on example with a simple RL problem.

Module 10: Model Selection and Hyperparameter Tuning

  • The importance of hyperparameter tuning for model performance.
  • Grid search and random search techniques.
  • Introduction to automated hyperparameter tuning with libraries like Optuna.
  • Systematic workflow for selecting the best model.
  • Practical lab: optimizing a model's performance.

Module 11: Machine Learning with Python

  • In-depth use of NumPy for numerical operations.
  • Using Pandas for data manipulation and analysis.
  • Visualizing data with Matplotlib and Seaborn.
  • Building a complete machine learning pipeline with scikit-learn.
  • Troubleshooting code and debugging models.

Module 12: Real-World Applications & Case Studies

  • Recommender systems: collaborative filtering and content-based methods.
  • Natural Language Processing (NLP) with an ML focus.
  • Image recognition and computer vision basics.
  • Case studies from finance, healthcare, and retail.
  • Project: building a small-scale recommendation engine.

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

 

 

Master Of Algorithms: An Introduction To Machine Learning Training Course in Namibia
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