Undergraduate Certificate in Machine Learning Basics for Data Analysts

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Overview

Learning outcomes

Course content

Introduction to Machine Learning

Data Preprocessing Techniques

Exploratory Data Analysis

Supervised Learning Fundamentals

Unsupervised Learning Concepts

Model Evaluation and Validation

Feature Engineering for Data Analysis

Introduction to Neural Networks

Time Series Analysis

Ethics and Bias in Machine Learning

Introduction to Machine Learning Basics

Data Preprocessing and Cleaning

Supervised Learning Algorithms

Unsupervised Learning Algorithms

Evaluation and Validation Techniques

Feature Selection and Engineering

Model Interpretability and Visualization

Clustering Techniques

Dimensionality Reduction Methods

Natural Language Processing Basics.

Introduction to Machine Learning

Foundations of Data Analysis

Supervised Learning Techniques

Unsupervised Learning Techniques

Model Evaluation and Selection

Feature Engineering

Data Preprocessing

Regression Analysis

Classification Methods

Natural Language Processing

Duration

The programme is available in two duration modes:
Fast track - 1 month
Standard mode - 2 months

Course fee

The fee for the programme is as follows:
Fast track - 1 month: GBP £140
Standard mode - 2 months: GBP £90

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Career Path

Key facts

Why this course

Frequently asked questions

This course equips data analysts with foundational machine learning concepts, techniques, and practical applications to enhance data-driven decision-making skills.

Showcase your achievement with a professional certificate upon successful course completion.

Undergraduate Certificate in Machine Learning Basics for Data Analysts

Overview

Learning outcomes

Course content

Introduction to Machine Learning

Data Preprocessing Techniques

Exploratory Data Analysis

Supervised Learning Fundamentals

Unsupervised Learning Concepts

Model Evaluation and Validation

Feature Engineering for Data Analysis

Introduction to Neural Networks

Time Series Analysis

Ethics and Bias in Machine Learning

Introduction to Machine Learning Basics

Data Preprocessing and Cleaning

Supervised Learning Algorithms

Unsupervised Learning Algorithms

Evaluation and Validation Techniques

Feature Selection and Engineering

Model Interpretability and Visualization

Clustering Techniques

Dimensionality Reduction Methods

Natural Language Processing Basics.

Introduction to Machine Learning

Foundations of Data Analysis

Supervised Learning Techniques

Unsupervised Learning Techniques

Model Evaluation and Selection

Feature Engineering

Data Preprocessing

Regression Analysis

Classification Methods

Natural Language Processing

Duration

Course fee

Career Path

Key facts

Why this course

Frequently asked questions

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Earn a Certificate

Skills gained