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

Are you ready to take your data skills to new heights? Get ready to embark on an exhilarating journey into the world of data science and machine learning! Introducing our cutting-edge Level 7 Advanced Diploma in Data Science & Machine Learning with Python - QLS Endorsed course, designed for aspiring data superheroes like yourself.

Our Level 7 Advanced Diploma in Data Science & Machine Learning with Python will equip you with the skills and knowledge to stand out in today's data-driven world. As a data superhero, 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 big data challenges with confidence.
• Create visually stunning data visualisations.
• Tell compelling stories with your data.

Don't miss out on this opportunity to become a data superhero! Enrol in our Level 7 Advanced Diploma in Data Science & Machine Learning with Python course, take your data skills to unprecedented heights and become an invaluable asset in any industry.

Why prefer this course?

There are many reasons to take our in-depth Level 7 Advanced Diploma in Data Science & Machine Learning with Python - QLS Endorsed course. Here are a few:

• 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.

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

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

Certificate of Achievement:

After successfully completing the Level 7 Advanced Diploma in Data Science & Machine Learning with Python - QLS Endorsed, 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.

This course is recommended for aspiring data scientists, 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 data manipulation, analysis, visualization, and building predictive models. Whether you are a beginner or have some prior experience in programming or data analysis, this course is designed to provide a comprehensive foundation and take your knowledge and expertise to the next level in the field of data science and machine learning.

No prior qualifications are needed for Learners to enrol on this Level 7 Advanced Diploma in Data Science & Machine Learning with Python - QLS Endorsed course.

After completing the Data Science & Machine Learning with Python online course, you can pursue the following career paths:

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

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