Level 7 Advanced Diploma in Data Science & Machine Learning with Python - QLS Endorsed

Data is the heartbeat of innovation — driving industries forward, informing smarter decisions, and shaping the future one algorithm at a time. The Level 7 Advanced Diploma in Data Science & Machine Learning with Python – QLS Endorsed course is your gateway into this dynamic world, where technical skill meets analytical insight and innovation becomes second nature. This course doesn't just equip you with skills — it instills a data-driven mindset for lasting success.

Throughout this comprehensive program, you’ll begin by mastering Python, the leading language in data science, before moving into the essentials of data preparation, analysis, and visualisation. You’ll learn how to build and optimise models using both supervised and unsupervised learning, explore advanced algorithms, and master techniques to evaluate and enhance model performance. Topics like neural networks, regression, classification, and clustering are all included, giving you the expertise to interpret complex data and create adaptive, intelligent systems.

Stay ahead of the curve in a rapidly evolving digital landscape — enrol in the Level 7 Advanced Diploma today and equip yourself with the knowledge and confidence to thrive.

Our Course will equip you with the skills and knowledge to stand out in today's data-driven world. You'll be able to:

• Analyse complex datasets and uncover valuable insights.
• Build accurate and robust machine learning models.
• Harness the power of deep learning and neural networks.
• Handle challenges with confidence.
• Tell compelling stories with your data.

CPD Accredited Digital Certificate

Digital certificate - Included

QLS Endorsed Hard Copy Certificate

Hard copy certificate - Included

CPD Accredited Hard Copy Certificate

Hard copy certificate - £15.99

Multiple Choice Questions (MCQ)/Assignment

Included in course price

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 file using Python Standard Library
• Load and Read CSV file using NumPy
• Load and Read CSV file using Pandas
• Peek, Dimensions and Types
• Class Distribution and Summary
• Explaining Correlation
• Explaining Skewness - Gaussian and Normal Curve
• Using Histograms
• Using Density Plots
• Box and Whisker Plots
• Multivariate Visualization - Correlation Plots
• Multivariate Visualization - Scatter Plots (part 1-6)
• Feature Selection - Introduction55
• 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
• 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 Modelling
• Pipelines : Feature Selection and 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
• Quick Session: Imbalanced Data Set - Issue Overview and Steps
• Finalizing Multi-Class
• Finalizing a Regression Model - The Boston Housing Price
• Real-time Predictions: Using the Pima Indian Diabetes Classification Model
• Real-time Predictions: Using Iris Flowers Multi-Class Classification
• Real-time Predictions: Using the Boston Housing Regression Model

Certificate of Achievement:

After successfully completing the Course, you will be eligible to order an original hardcopy certificate of achievement endorsed by the Quality Licence Scheme. The certificate will be delivered at your doorstep completely free of charge.

Why prefer this course?

• QLS endorsed course.
• From Monday through Friday, complete tutor support is offered.
• Full access to course materials for a full year.
• Immediate assessment findings
• A free PDF certificate (CPD) is awarded after passing the course.
• Study the subject of the course at your own pace.
• Simple-to-understand modules and are taught by professionals
• Ask our email and live chat teams for assistance or guidance at any time.
• Utilise your computer, tablet, or mobile device to study at your own pace while finishing the course.

This course is recommended for aspiring analysts, and professionals from various backgrounds who are interested in leveraging the power of Python for data science and machine learning. It is suitable for individuals seeking to enhance their skills in manipulation, analysis, visualization, and building predictive models. Whether you are a beginner or have some prior experience, this course is designed to provide a comprehensive foundation and take your knowledge and expertise to the next level in the field.

No prior qualifications are needed for Learners to enrol on this course.

After completing the course, you can pursue the following career paths:

• Data Scientist
• Data Analyst
• Business Analyst
• Data Engineer
• AI Researcher
• Predictive Modeler
• Quantitative Analyst
• Data Consultant
• Research Scientist

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