Data Science & Machine Learning with Python
Course
Online
Description
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Type
Course
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Level
Intermediate
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Methodology
Online
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Class hours
10h
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Duration
1 Year
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Online campus
Yes
The Data Science & Machine Learning with Python course equips learners with the essential skills to analyse complex datasets and develop predictive models using Python. Covering foundational programming, data manipulation, statistical analysis, and machine learning algorithms, this CPD course enables students to understand real-world data challenges and apply intelligent solutions. Learners will explore Python libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn, mastering data cleaning, visualisation, and model evaluation. This course is ideal for anyone looking to enhance their data-driven decision-making capabilities, improve career prospects in tech, analytics, or business intelligence, and add a valuable skill set to their CV. With self-paced, online learning, participants can study flexibly and gain practical expertise applicable across industries. By the end of the course, learners will confidently build, assess, and optimise machine learning models, preparing them for roles such as Data Analyst, Data Scientist, or AI Specialist.
Important information
Price for Emagister users:
About this course
Understand Python programming for data science.
Perform data cleaning and preprocessing efficiently.
Analyse and visualise datasets using Python libraries.
Apply machine learning algorithms for predictive modelling.
Evaluate model performance and optimise solutions.
Develop practical data-driven decision-making skills.
Enhance CV with marketable data science competencies.
This course is suitable for learners aged 16+ who are keen to build a career in data science, machine learning, or analytics. It is ideal for students, professionals, or career changers who wish to enhance their understanding of Python programming and data-driven methodologies. Individuals interested in roles such as Data Analyst, Business Analyst, Machine Learning Engineer, or AI Specialist will benefit from the skills gained. The course is designed to be accessible and inclusive, providing a structured path to develop technical expertise without prior experience in programming or advanced mathematics. Anyone looking to improve problem-solving abilities and strengthen their CV will find this course valuable.
No formal entry requirements are needed for this course. It is suitable for learners aged 16 and above. A good level of English, numeracy, and basic IT skills is recommended to ensure participants can comfortably engage with the course material and Python programming exercises.
Upon successful completion of the Data Science & Machine Learning with Python, you will qualify for a UK and internationally recognised professional certification. You may also choose to formalise your achievement by obtaining your PDF Certificate for £9 or a Hardcopy Certificate for £15.
The Data Science & Machine Learning with Python course offers flexible, self-paced learning to suit individual schedules. The modules are designed by experts, focusing on practical, career-oriented skills that improve employability and enhance your CV. Learners gain a comprehensive understanding of Python, data analysis, and machine learning, ensuring knowledge can be applied directly to professional scenarios. The course structure allows learners to progress at their own pace, with content tailored to both beginners and those with some prior experience. By combining theoretical understanding with applied exercises, this course delivers value for personal and professional development.
Yes, the course is designed to be accessible for beginners with no prior programming experience. Modules guide learners step by step, building knowledge from Python basics to advanced machine learning concepts in a clear, structured manner.
Completing this course equips learners with in-demand skills in Python, data analysis, and machine learning, enhancing employability across industries. It prepares you for roles such as Data Analyst, AI Specialist, and Data Scientist, and strengthens your CV for career advancement.
The course is fully online and self-paced, allowing learners to study at their convenience. Content includes interactive modules, exercises, and assessments, enabling practical understanding of data science concepts without needing to attend in-person sessions.
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The average rating is higher than 3.7
More than 50 reviews in the last 12 months
This centre has featured on Emagister for 7 years
Subjects
- Evaluation
- Data analysis
- Algorithms
- Programming
- Data science
Teachers and trainers (1)
One Education
Course Provider
Course programme
The Data Science & Machine Learning with Python course introduces key concepts in data analysis and machine learning, covering Python programming, data handling, visualisation, statistical basics, and introductory machine learning models. It helps learners build foundational skills to analyse data, identify patterns, and apply machine learning techniques for informed decision-making.
Course Curriculum
- Course Overview & Table of Contents
Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types
Introduction to Machine Learning - Part 2 - Classifications and Applications
System and Environment preparation - Part 1
System and Environment preparation - Part 2
Learn Basics of python - Assignment
Learn Basics of python - Assignment
Learn Basics of python - Functions
Learn Basics of python - Data Structures
Learn Basics of NumPy - NumPy Array
Learn Basics of NumPy - NumPy Data
Learn Basics of NumPy - NumPy Arithmetic
Learn Basics of Matplotlib
Learn Basics of Pandas - Part 1
Learn Basics of Pandas - Part 2
Understanding the CSV data file
Load and Read CSV data file using Python Standard Library
Load and Read CSV data file using NumPy
Load and Read CSV data file using Pandas
Dataset Summary - Peek, Dimensions and Data Types
Dataset Summary - Class Distribution and Data Summary
Dataset Summary - Explaining Correlation
Dataset Summary - Explaining Skewness - Gaussian and Normal Curve
Dataset Visualization - Using Histograms
Dataset Visualization - Using Density Plots
Dataset Visualization - Box and Whisker Plots
Multivariate Dataset Visualization - Correlation Plots
Multivariate Dataset Visualization - Scatter Plots
Data Preparation (Pre-Processing) - Introduction
Data Preparation - Re-scaling Data - Part 1
Data Preparation - Re-scaling Data - Part 2
Data Preparation - Standardizing Data - Part 1
Data Preparation - Standardizing Data - Part 2
Data Preparation - Normalizing Data
Data Preparation - Binarizing Data
Feature Selection - Introduction
Feature Selection - Uni-variate Part 1 - Chi-Squared Test
Feature Selection - Uni-variate Part 2 - Chi-Squared Test
Feature Selection - Recursive Feature Elimination
Feature Selection - Principal Component Analysis (PCA)
Feature Selection - Feature Importance
Refresher Session - The Mechanism of Re-sampling, Training and Testing
Algorithm Evaluation Techniques - Introduction
Algorithm Evaluation Techniques - Train and Test Set
Algorithm Evaluation Techniques - K-Fold Cross Validation
Algorithm Evaluation Techniques - Leave One Out Cross Validation
Algorithm Evaluation Techniques - Repeated Random Test-Train Splits
Algorithm Evaluation Metrics - Introduction
Algorithm Evaluation Metrics - Classification Accuracy
Algorithm Evaluation Metrics - Log Loss
Algorithm Evaluation Metrics - Area Under ROC Curve
Algorithm Evaluation Metrics - Confusion Matrix
Algorithm Evaluation Metrics - Classification Report
Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction
Algorithm Evaluation Metrics - Mean Absolute Error
Algorithm Evaluation Metrics - Mean Square Error
Algorithm Evaluation Metrics - R Squared
Classification Algorithm Spot Check - Logistic Regression
Classification Algorithm Spot Check - Linear Discriminant Analysis
Classification Algorithm Spot Check - K-Nearest Neighbors
Classification Algorithm Spot Check - Naive Bayes
Classification Algorithm Spot Check - CART
Classification Algorithm Spot Check - Support Vector Machines
Regression Algorithm Spot Check - Linear Regression
Regression Algorithm Spot Check - Ridge Regression
Regression Algorithm Spot Check - Lasso Linear Regression
Regression Algorithm Spot Check - Elastic Net Regression
Regression Algorithm Spot Check - K-Nearest Neighbors
Regression Algorithm Spot Check - CART
Regression Algorithm Spot Check - Support Vector Machines (SVM)
Compare Algorithms - Part 1 : Choosing the best Machine Learning Model
Compare Algorithms - Part 2 : Choosing the best Machine Learning Model
Pipelines : Data Preparation and Data Modelling
Pipelines : Feature Selection and Data Modelling
Performance Improvement: Ensembles - Voting
Performance Improvement: Ensembles - Bagging
Performance Improvement: Ensembles - Boosting
Performance Improvement: Parameter Tuning using Grid Search
Performance Improvement: Parameter Tuning using Random Search
Export, Save and Load Machine Learning Models : Pickle
Export, Save and Load Machine Learning Models : Joblib
Finalizing a Model - Introduction and Steps
Finalizing a Classification Model - The Pima Indian Diabetes Dataset
Quick Session: Imbalanced Data Set - Issue Overview and Steps
Iris Dataset : Finalizing Multi-Class Dataset
Finalizing a Regression Model - The Boston Housing Price Dataset
Real-time Predictions: Using the Pima Indian Diabetes Classification Model
Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
Real-time Predictions: Using the Boston Housing Regression Model
Data Science & Machine Learning with Python