Course Overview

The Data Science & Machine Learning with Python course offers a thorough introduction to Python programming and its application in data science. You will learn how to manipulate data, apply machine learning models, and evaluate them using various theoretical frameworks. This course covers the core concepts of machine learning, data transformation, and model evaluation, all delivered through structured learning content.

Key topics covered include:

Introduction to Python programming for data analysis
Understanding core machine learning algorithms such as classification, regression, and clustering
Exploring data preparation and pre-processing techniques, including feature selection
Applying data visualisation techniques to understand datasets
Evaluating models using metrics like accuracy, precision, recall, and F1-score
Learning about machine learning tools like NumPy, Pandas, and Matplotlib for theoretical applications
Discussing model evaluation strategies such as cross-validation and performance metrics

Learning Outcomes

By the end of the Data Science & Machine Learning with Python course, you will be able to:

Understand Python programming fundamentals for data analysis
Identify and apply various machine learning algorithms in a theoretical context
Analyse and manipulate datasets using Python libraries like Pandas and NumPy
Understand data visualisation techniques using tools such as Matplotlib
Evaluate machine learning models based on common performance metrics
Learn about feature selection techniques and their impact on machine learning models
Understand the theoretical underpinnings of machine learning algorithms and their applications

Why Enrol in This Course?

As data-driven decision-making becomes increasingly important across industries, professionals with expertise in data science and machine learning are in high demand. This course provides you with the necessary theoretical knowledge to understand how data science and machine learning work and how they can be applied in various industries. With CPD-accredited certification, this course allows you to deepen your understanding of Python and machine learning concepts.

What you gain:

CPD-accredited certification to support your career development
In-depth knowledge of data science principles and machine learning algorithms
Lifetime access to the course materials for continued learning at your own pace

Who is This Course for?

This course is ideal for:

Individuals looking to develop a strong theoretical understanding of data science and machine learning
Those interested in pursuing a career in data analysis or machine learning
Students or professionals who wish to enhance their knowledge of Python and its application in data science
Anyone interested in the theoretical aspects of machine learning algorithms and model evaluation

Prerequisites for This Course

There are no formal prerequisites for this course. It is suitable for beginners who wish to gain an understanding of Python and machine learning principles.

Certification

Upon completing the course and passing the final exam, you will receive a CPD QS Accredited Certificate from Apex Learning. This certification demonstrates your qualifications and skills. The Digital version of the certificate is available for a discounted price of £9.99 only, and you can order a hard copy of the certificate for a discounted price of £14.99 only.

📍Note: Discounted certificate pricing is available for a limited time only. Secure yours before the offer ends!

Career Path

Completing the Data Science & Machine Learning with Python course provides the knowledge needed for various theoretical roles in data science, including:

Data Scientist – £35,000 to £60,000 annually
Apply data science principles to analyse and model complex datasets.
Machine Learning Engineer – £40,000 to £70,000 annually
Work with machine learning algorithms to create predictive models in various sectors.
Data Analyst – £25,000 to £45,000 annually
Use theoretical knowledge of data analysis tools to interpret data and provide insights.
Business Intelligence Analyst – £30,000 to £50,000 annually
Interpret data to support business decisions and visualise business intelligence metrics.

Data Science & Machine Learning with Python | CPD Accredited Course Online – UK

Data Science and Machine Learning are essential fields for analysing complex datasets and building predictive models. This CPD-accredited course offers comprehensive training in Python programming, data manipulation, machine learning algorithms, and model evaluation. Ideal for those who wish to enhance their theoretical understanding of these subjects, this course provides the foundation to work effectively with data in any context.

Course Overview

Key topics covered include:

Introduction to Python programming for data analysis
Understanding core machine learning algorithms such as classification, regression, and clustering
Exploring data preparation and pre-processing techniques, including feature selection
Applying data visualisation techniques to understand datasets
Evaluating models using metrics like accuracy, precision, recall, and F1-score
Learning about machine learning tools like NumPy, Pandas, and Matplotlib for theoretical applications
Discussing model evaluation strategies such as cross-validation and performance metrics

Learning Outcomes

By the end of the Data Science & Machine Learning with Python course, you will be able to:

Understand Python programming fundamentals for data analysis
Identify and apply various machine learning algorithms in a theoretical context
Analyse and manipulate datasets using Python libraries like Pandas and NumPy
Understand data visualisation techniques using tools such as Matplotlib
Evaluate machine learning models based on common performance metrics
Learn about feature selection techniques and their impact on machine learning models
Understand the theoretical underpinnings of machine learning algorithms and their applications

Why Enrol in This Course?

What you gain:

CPD-accredited certification to support your career development
In-depth knowledge of data science principles and machine learning algorithms
Lifetime access to the course materials for continued learning at your own pace

Who is This Course for?

This course is ideal for:

Individuals looking to develop a strong theoretical understanding of data science and machine learning
Those interested in pursuing a career in data analysis or machine learning
Students or professionals who wish to enhance their knowledge of Python and its application in data science
Anyone interested in the theoretical aspects of machine learning algorithms and model evaluation

Prerequisites

There are no formal prerequisites for this course. It is suitable for beginners who wish to gain an understanding of Python and machine learning principles.

Assessment Method

Assessment for the course is conducted through an automated multiple-choice exam. A score of 60% is required to pass and earn the CPD Accredited Certificate. Reflective assignments are provided to enhance your learning, with expert tutor feedback available.

Certification

📍Note: Discounted certificate pricing is available for a limited time only. Secure yours before the offer ends!

Career Path

Completing the Data Science & Machine Learning with Python course provides the knowledge needed for various theoretical roles in data science, including:

Data Scientist – £35,000 to £60,000 annually
Apply data science principles to analyse and model complex datasets.
Machine Learning Engineer – £40,000 to £70,000 annually
Work with machine learning algorithms to create predictive models in various sectors.
Data Analyst – £25,000 to £45,000 annually
Use theoretical knowledge of data analysis tools to interpret data and provide insights.
Business Intelligence Analyst – £30,000 to £50,000 annually
Interpret data to support business decisions and visualise business intelligence metrics.

Frequestly Asked Questions

Is the Data Science & Machine Learning with Python course fully online?

Yes, the course is delivered entirely online, allowing you to learn at your own pace and convenience.

Do I need prior programming experience to take this course?

No, this course is designed for beginners and does not require prior experience in programming.

Will I receive a certificate upon completion of the course?

Yes, you will receive a CPD-accredited certificate upon successfully completing the course.

How long will I have access to the course materials?

You will have lifetime access to all course content, allowing you to revisit the material whenever necessary.

How long does it take to complete the course?

The course is self-paced, and most learners complete it in approximately 6 to 8 weeks, depending on their study schedule.

Course Curriculum

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4 sections
8 lectures
00:00:00 total length

Expand all sections

Data Science & Machine Learning with Python

Course Overview & Table of Contents

00:09:00
Introduction to Machine Learning – Part 1 – Concepts , Definitions and Types

00:05:00
Introduction to Machine Learning – Part 2 – Classifications and Applications

00:06:00
System and Environment preparation – Part 1

00:08:00
System and Environment preparation – Part 2

00:06:00
Learn Basics of python – Assignment 1

00:10:00
Learn Basics of python – Assignment 2

00:09:00
Learn Basics of python – Functions

00:04:00
Learn Basics of python – Data Structures

00:12:00
Learn Basics of NumPy – NumPy Array

00:06:00
Learn Basics of NumPy – NumPy Data

00:08:00
Learn Basics of NumPy – NumPy Arithmetic

00:04:00
Learn Basics of Matplotlib

00:07:00
Learn Basics of Pandas – Part 1

00:06:00
Learn Basics of Pandas – Part 2

00:07:00
Understanding the CSV data file

00:09:00
Load and Read CSV data file using Python Standard Library

00:09:00
Load and Read CSV data file using NumPy

00:04:00
Load and Read CSV data file using Pandas

00:05:00
Dataset Summary – Peek, Dimensions and Data Types

00:09:00
Dataset Summary – Class Distribution and Data Summary

00:09:00
Dataset Summary – Explaining Correlation

00:11:00
Dataset Summary – Explaining Skewness – Gaussian and Normal Curve

00:07:00
Dataset Visualization – Using Histograms

00:07:00
Dataset Visualization – Using Density Plots

00:06:00
Dataset Visualization – Box and Whisker Plots

00:05:00
Multivariate Dataset Visualization – Correlation Plots

00:08:00
Multivariate Dataset Visualization – Scatter Plots

00:05:00
Data Preparation (Pre-Processing) – Introduction

00:09:00
Data Preparation – Re-scaling Data – Part 1

00:09:00
Data Preparation – Re-scaling Data – Part 2

00:09:00
Data Preparation – Standardizing Data – Part 1

00:07:00
Data Preparation – Standardizing Data – Part 2

00:04:00
Data Preparation – Normalizing Data

00:08:00
Data Preparation – Binarizing Data

00:06:00
Feature Selection – Introduction

00:07:00
Feature Selection – Uni-variate Part 1 – Chi-Squared Test

00:09:00
Feature Selection – Uni-variate Part 2 – Chi-Squared Test

00:10:00
Feature Selection – Recursive Feature Elimination

00:11:00
Feature Selection – Principal Component Analysis (PCA)

00:09:00
Feature Selection – Feature Importance

00:07:00
Refresher Session – The Mechanism of Re-sampling, Training and Testing

00:12:00
Algorithm Evaluation Techniques – Introduction

00:07:00
Algorithm Evaluation Techniques – Train and Test Set

00:11:00
Algorithm Evaluation Techniques – K-Fold Cross Validation

00:09:00
Algorithm Evaluation Techniques – Leave One Out Cross Validation

00:05:00
Algorithm Evaluation Techniques – Repeated Random Test-Train Splits

00:07:00
Algorithm Evaluation Metrics – Introduction

00:09:00
Algorithm Evaluation Metrics – Classification Accuracy

00:08:00
Algorithm Evaluation Metrics – Log Loss

00:03:00
Algorithm Evaluation Metrics – Area Under ROC Curve

00:06:00
Algorithm Evaluation Metrics – Confusion Matrix

00:10:00
Algorithm Evaluation Metrics – Classification Report

00:04:00
Algorithm Evaluation Metrics – Mean Absolute Error – Dataset Introduction

00:06:00
Algorithm Evaluation Metrics – Mean Absolute Error

00:07:00
Algorithm Evaluation Metrics – Mean Square Error

00:03:00
Algorithm Evaluation Metrics – R Squared

00:04:00
Classification Algorithm Spot Check – Logistic Regression

00:12:00
Classification Algorithm Spot Check – Linear Discriminant Analysis

00:04:00
Classification Algorithm Spot Check – K-Nearest Neighbors

00:05:00
Classification Algorithm Spot Check – Naive Bayes

00:04:00
Classification Algorithm Spot Check – CART

00:04:00
Classification Algorithm Spot Check – Support Vector Machines

00:05:00
Regression Algorithm Spot Check – Linear Regression

00:08:00
Regression Algorithm Spot Check – Ridge Regression

00:03:00
Regression Algorithm Spot Check – Lasso Linear Regression

00:03:00
Regression Algorithm Spot Check – Elastic Net Regression

00:02:00
Regression Algorithm Spot Check – K-Nearest Neighbors

00:06:00
Regression Algorithm Spot Check – CART

00:04:00
Regression Algorithm Spot Check – Support Vector Machines (SVM)

00:04:00
Compare Algorithms – Part 1 : Choosing the best Machine Learning Model

00:09:00
Compare Algorithms – Part 2 : Choosing the best Machine Learning Model

00:05:00
Pipelines : Data Preparation and Data Modelling

00:11:00
Pipelines : Feature Selection and Data Modelling

00:10:00
Performance Improvement: Ensembles – Voting

00:07:00
Performance Improvement: Ensembles – Bagging

00:08:00
Performance Improvement: Ensembles – Boosting

00:05:00
Performance Improvement: Parameter Tuning using Grid Search

00:08:00
Performance Improvement: Parameter Tuning using Random Search

00:06:00
Export, Save and Load Machine Learning Models : Pickle

00:10:00
Export, Save and Load Machine Learning Models : Joblib

00:06:00
Finalizing a Model – Introduction and Steps

00:07:00
Finalizing a Classification Model – The Pima Indian Diabetes Dataset

00:07:00
Quick Session: Imbalanced Data Set – Issue Overview and Steps

00:09:00
Iris Dataset : Finalizing Multi-Class Dataset

00:09:00
Finalizing a Regression Model – The Boston Housing Price Dataset

00:08:00
Real-time Predictions: Using the Pima Indian Diabetes Classification Model

00:07:00
Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset

00:03:00
Real-time Predictions: Using the Boston Housing Regression Model

00:08:00