Machine Learning Basics Online Training

The Machine Learning Basics Online Training is a foundational course designed to introduce learners to core concepts, algorithms, and practical tools in machine learning. This training simplifies the technical depth of data-driven modeling, enabling learners to build, test, and optimize models through regression, classification, and decision trees. From understanding the role of predictors to applying statistical tools like Minitab, each section supports clear and actionable insights.

This course gives participants the opportunity to work with real-world data, enabling meaningful hands-on experience in regression trees, binary logistic regression, and classification models. Learners will also understand the significance of data preparation through effective data cleaning techniques and learn how to structure, evaluate, and deploy data models to improve prediction accuracy.

Whether you're just getting started or seeking structured clarity, this course provides the essential roadmap to machine learning success with applied statistical techniques, basic coding logic, and model interpretation.

Learning Outcomes

1. Understand regression techniques for modeling continuous data accurately.
2. Apply predictor variables to build effective learning algorithms.
3. Utilize Minitab for statistical analysis and model evaluation.
4. Develop regression and classification trees using real-world data.
5. Clean and prepare datasets to optimize learning model outputs.
6. Build structured models to enhance machine learning accuracy.

Course Link : Machine Learning Basics Online Training

Course Curriculum

• Section 01: Introduction
  Gain an overview of machine learning principles and data-driven logic.
• Section 02: Regression
  Learn to model linear relationships and continuous variables accurately.
• Section 03: Predictors
  Understand independent variables and their predictive influence on models.
• Section 04: Minitab
  Use Minitab software to run statistical regressions and diagnostics.
• Section 05: Regression Trees
  Explore non-linear regression techniques through decision tree models.
• Section 06: Binary Logistics Regression
  Classify binary outcomes using logistic regression frameworks.
• Section 07: Classification Trees
  Develop tree-based models for categorical variable classification.
• Section 08: Data Cleaning
  Prepare and clean datasets to improve model performance and reliability.
• Section 09: Data Models
  Construct data models to forecast trends and patterns effectively.
• Section 10: Learning Success
  Review strategies and skills for continued success in machine learning.

Disclaimer:

This is an online course with pre-recorded lessons. You will get access to the course within 48 hours after your enrolment.