Machine Learning Fundamentals
Uptrail
Master Machine Learning with Python. Build predictive models using Scikit-Learn, Linear Regression and Random Forests.
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Overview
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Reed Courses Certificate of Completion
Digital certificate
- Show recruiters you've mastered in-demand skills
- Boost your CV and job applications instantly
- Share on LinkedIn to stand out from other candidates
- Transcript of completion included
Curriculum
This course contains
Format: 3 Videos (with subtitles and transcripts)
Duration: 1h and 53m
Description
This course is structured into seven comprehensive modules designed to build your expertise step-by-step:
Module 01: Introduction to Machine Learning – Understand the fundamental differences between Supervised, Unsupervised, and Reinforcement learning.
Module 02: Linear Regression – Master the basics of predictive modelling, including cost functions, gradient descent, and multi-variable regression.
Module 03: Classification Algorithms – Implement and compare Logistic Regression, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN).
Module 04: Decision Trees & Random Forests – Learn to build robust ensemble models that handle non-linear data and improve prediction accuracy.
Module 05: Unsupervised Learning – Explore pattern discovery through K-Means clustering and Dimensionality Reduction (PCA).
Module 06: Model Evaluation & Tuning – Learn to combat Overfitting and Underfitting using Cross-Validation and Hyperparameter optimization.
Module 07: Practical Applications – Apply your skills to real-world datasets to solve classification and regression problems.
Who is this course for?
Data Analysts looking to transition into predictive analytics and automated modelling.
Software Developers who want to integrate Machine Learning features into their applications.
Students in technical fields seeking a hands-on supplement to their theoretical studies.
Aspiring Data Scientists building a portfolio of projects using Scikit-Learn.
Requirements
A basic understanding of Python programming (loops, functions, and variables).
Familiarity with data libraries like Pandas and NumPy is helpful but not required.
Fundamental knowledge of high-school level mathematics (algebra and statistics).
A computer capable of running Python via Jupyter Notebooks, Anaconda, or VS Code.
Career path
Completing this course provides the technical skills for roles such as:
Junior Machine Learning Engineer
Data Scientist
Predictive Analyst
AI Developer
Business Intelligence (BI) Analyst
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This course is advertised on Reed.co.uk by the Course Provider, whose terms and conditions apply. Purchases are made directly from the Course Provider, and as such, content and materials are supplied by the Course Provider directly. Reed is acting as agent and not reseller in relation to this course. Reed's only responsibility is to facilitate your payment for the course. It is your responsibility to review and agree to the Course Provider's terms and conditions and satisfy yourself as to the suitability of the course you intend to purchase. Reed will not have any responsibility for the content of the course and/or associated materials.
