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

Imagine harnessing the power of data to unlock mysteries and innovate solutions. The Level 7 Advanced Diploma in Data Science & Machine Learning with Python course is your gateway to this realm. This Data Science & Machine Learning with Python course takes you from foundational Python programming to advanced machine learning techniques, crafting your journey in the exciting world of data science.

This Advanced Diploma in Data Science & Machine Learning with Python at QLS Level 7 course is not just an educational journey but a gateway to the forefront of the UK's data revolution. With industries investing billions in data analytics, mastering machine learning with Python places you at the heart of innovation, shaping tomorrow's digital landscape. Join us, and be a part of this transformational wave.

Learning Outcomes

• Understand foundational machine learning concepts and applications.
• Master Python basics and apply advanced data analysis libraries.
• Interpret and visualise datasets for deeper insights.
• Evaluate machine learning algorithms using various metrics.
• Optimise model performance through ensembles and parameter tuning.
• Finalise and deploy models for real-time predictions.

What You Get Out Of Studying With Janets

• Free PDF certificate upon successful completion of the Data Science & Machine Learning with Python course
• Lifetime access to Data Science & Machine Learning with Python course materials
• Instant assessment results
• Full tutor support is available from Monday to Friday
• Study the Data Science & Machine Learning with Python course at your own pace
• Accessible, informative modules taught by expert instructors
• Get 24/7 help or advice from our email and live chat teams with the Data Science & Machine Learning with Python course
• Study at your own time through your computer, tablet or mobile device
• Improve your chance of gaining valuable skills by completing the Data Science & Machine Learning with Python course

Enrol in the Advanced Diploma in Data Science & Machine Learning with Python at QLS Level 7 course to find out more about the topic and get one step closer to reaching your desired success!!

Dive deep into the world of data science and machine learning with this comprehensive Advanced Diploma in Data Science & Machine Learning with Python at QLS Level 7 course. Throughout the Data Science & Machine Learning with Python course, learners will be immersed in the foundational concepts of machine learning, coupled with hands-on Python programming exercises.

Essential libraries such as NumPy, Pandas, and Matplotlib are explored to enable robust data analysis. Participants will also journey through a myriad of algorithm evaluation techniques, refining their ability to select and optimise the most effective models. By the end of the Data Science & Machine Learning with Python course, learners will be adept at finalising and deploying models to make real-time predictions.

Data Science & Machine Learning with Python 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 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
• 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
• 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
• 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
• 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
• Resources

Method of Assessment

To successfully complete the course, students will have to take an automated multiple-choice exam. This exam will be online, and you will need to score 60% or above to pass the course. After successfully passing the course exam, you will be able to apply for a certificate as proof of your expertise.

This course is designed for:

• Aspiring data scientists seeking a comprehensive education in data science and machine learning.
• Professionals in IT and analytics aiming to broaden their knowledge in Python and machine learning.
• Academics and researchers interested in applying data science methodologies in their work.
• Business analysts wanting to leverage data science for more insightful data interpretation.
• Technology enthusiasts eager to explore the potential of machine learning and Python in solving complex problems.

No prior qualifications are needed for learners to enrol in this course.

After completing this course, you can pursue the following career pathways:

• Data Scientist - Average Salary: £40,000 - £60,000
• Machine Learning Engineer - Average Salary: £50,000 - £70,000
• Data Analyst - Average Salary: £30,000 - £45,000
• Research Scientist - Average Salary: £45,000 - £65,000
• Algorithm Developer - Average Salary: £45,000 - £55,000

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