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Overview

Overview

Welcome to the exciting world of Data Science and Machine Learning with Python! This online course is meticulously crafted to equip you with essential skills and knowledge necessary to thrive in today’s data-driven era. Whether you’re new to programming or looking to expand your expertise, this course offers a structured path to mastering Python for data science applications.

The course begins with a solid foundation in Python programming. Even if you’re a complete beginner, you’ll swiftly grasp core concepts and syntax, setting the stage for deeper exploration. With Python as our tool of choice, we’ll delve into data manipulation using libraries like NumPy and Pandas. You’ll learn to efficiently handle and preprocess data, essential for any data science project.

Next, we pivot to data visualization—a crucial skill for understanding and communicating insights. Through hands-on exercises using Matplotlib and Seaborn, you’ll transform raw data into meaningful visualizations that reveal patterns, trends, and anomalies.

As we progress, we shift gears into the heart of data science: machine learning. You’ll explore various machine learning algorithms, from foundational linear and logistic regression to advanced techniques like decision trees and ensemble methods. Understanding these algorithms empowers you to build predictive models that uncover hidden patterns in data and make informed predictions.

Throughout the course, practical projects and real-world examples will reinforce your learning. By tackling hands-on challenges, such as predicting housing prices based on historical data or classifying customer behavior for targeted marketing, you’ll gain the confidence to apply your newfound skills in diverse scenarios.

By the course’s conclusion, you’ll have not only a robust understanding of Python’s capabilities for data science and machine learning but also a portfolio of projects demonstrating your proficiency. Whether you aspire to launch a career in data science, enhance your current role with data-driven insights, or simply satisfy your curiosity about the field, this course provides the necessary tools and knowledge to succeed.

Join us on this transformative journey into the realm of Data Science and Machine Learning with Python, and unlock your potential to extract actionable insights from data, influence decision-making, and drive innovation in any industry.

Learning Outcomes

  • Gain proficiency in Python programming for data science applications.
  • Understand fundamental concepts of data manipulation and analysis.
  • Learn to visualize data using Matplotlib and Seaborn.
  • Implement machine learning algorithms such as regression, classification, and clustering.
  • Develop practical skills through hands-on projects and case studies.

What Will Make You Stand Out?

On Completion of this online course, you’ll acquire:

  • CPD QS Accredited Course.
  • Upon Course Completion, Earn a Complimentary PDF and a hard copy Certificate to Showcase Your Enhanced Skills.
  • Lifetime access.
  • 24x7 Tutor Support.
  • Complete the course at your own pace.

Description

This course is your gateway to the dynamic fields of Data Science and Machine Learning using Python. Starting with the basics of Python programming, you’ll quickly progress to exploring data manipulation with Pandas and data visualization with Matplotlib and Seaborn. As you advance, dive deep into machine learning techniques including supervised and unsupervised learning, and learn to evaluate model performance. Practical, real-world projects throughout the course will reinforce your learning, allowing you to apply your skills to solve relevant data problems. Whether you’re aiming to advance your career or delve into a new field, this course provides the foundational knowledge and hands-on experience needed to excel in data-driven industries.

Who is this course for?

This course is ideal for aspiring data scientists, analysts, and anyone keen on leveraging Python for data-driven insights. It suits beginners looking to start a career in data science as well as professionals seeking to enhance their skills in Python programming and machine learning techniques.

Requirements

Access to a computer with internet connectivity and a desire to learn and succeed in your home-based business venture. No prior experience or qualifications are necessary.

Certification

Upon successful completion of the Data Science & Machine Learning with Python course, learners can obtain both a PDF certificate and a Hard copy certificate for completely FREE. The Hard copy certificate is available for a nominal fee of £3.99, which covers the delivery charge within the United Kingdom. Additional delivery charges may apply for orders outside the United Kingdom.

Career Path

  • Data Scientist: Analyze large datasets, build predictive models, and drive data-driven decisions.
  • Machine Learning Engineer: Develop and deploy machine learning solutions to automate processes and improve efficiency.
  • Data Analyst: Interpret data, generate reports, and communicate findings to stakeholders effectively.

Course Curriculum

Course Overview & Table of Contents
Course Overview & Table of Contents 00:09:00
Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types
Introduction to Machine Learning – Part 1 – Concepts , Definitions and Types 00:05:00
Introduction to Machine Learning - Part 2 - Classifications and Applications
Introduction to Machine Learning – Part 2 – Classifications and Applications 00:06:00
System and Environment preparation - Part 1
System and Environment preparation – Part 1 00:04:00
System and Environment preparation - Part 2
System and Environment preparation – Part 2 00:06:00
Learn Basics of python - Assignment
Learn Basics of python – Assignment 1 00:10:00
Learn Basics of python - Assignment
Learn Basics of python – Assignment 2 00:09:00
Learn Basics of python - Functions
Learn Basics of python – Functions 00:04:00
Learn Basics of python - Data Structures
Learn Basics of python – Data Structures 00:12:00
Learn Basics of NumPy - NumPy Array
Learn Basics of NumPy – NumPy Array 00:06:00
Learn Basics of NumPy - NumPy Data
Learn Basics of NumPy – NumPy Data 00:08:00
Learn Basics of NumPy - NumPy Arithmetic
Learn Basics of NumPy – NumPy Arithmetic 00:04:00
Learn Basics of Matplotlib
Learn Basics of Matplotlib 00:07:00
Learn Basics of Pandas - Part 1
Learn Basics of Pandas – Part 1 00:06:00
Learn Basics of Pandas - Part 2
Learn Basics of Pandas – Part 2 00:07:00
Understanding the CSV data file
Understanding the CSV data file 00:09:00
Load and Read CSV data file using Python Standard Library
Load and Read CSV data file using Python Standard Library 00:09:00
Load and Read CSV data file using NumPy
Load and Read CSV data file using NumPy 00:04:00
Load and Read CSV data file using Pandas
Load and Read CSV data file using Pandas 00:05:00
Dataset Summary - Peek, Dimensions and Data Types
Dataset Summary – Peek, Dimensions and Data Types 00:09:00
Dataset Summary - Class Distribution and Data Summary
Dataset Summary – Class Distribution and Data Summary 00:09:00
Dataset Summary - Explaining Correlation
Dataset Summary – Explaining Correlation 00:11:00
Dataset Summary - Explaining Skewness - Gaussian and Normal Curve
Dataset Summary – Explaining Skewness – Gaussian and Normal Curve 00:07:00
Dataset Visualization - Using Histograms
Dataset Visualization – Using Histograms 00:07:00
Dataset Visualization - Using Density Plots
Dataset Visualization – Using Density Plots 00:06:00
Dataset Visualization - Box and Whisker Plots
Dataset Visualization – Box and Whisker Plots 00:05:00
Multivariate Dataset Visualization - Correlation Plots
Multivariate Dataset Visualization – Correlation Plots 00:08:00
Multivariate Dataset Visualization - Scatter Plots
Multivariate Dataset Visualization – Scatter Plots 00:05:00
Data Preparation (Pre-Processing) - Introduction
Data Preparation (Pre-Processing) – Introduction 00:09:00
Data Preparation - Re-scaling Data - Part 1
Data Preparation – Re-scaling Data – Part 1 00:09:00
Data Preparation - Re-scaling Data - Part 2
Data Preparation – Re-scaling Data – Part 2 00:09:00
Data Preparation - Standardizing Data - Part 1
Data Preparation – Standardizing Data – Part 1 00:07:00
Data Preparation - Standardizing Data - Part 2
Data Preparation – Standardizing Data – Part 2 00:04:00
Data Preparation - Normalizing Data
Data Preparation – Normalizing Data 00:08:00
Data Preparation - Binarizing Data
Data Preparation – Binarizing Data 00:06:00
Feature Selection - Introduction
Feature Selection – Introduction 00:07:00
Feature Selection - Uni-variate Part 1 - Chi-Squared Test
Feature Selection – Uni-variate Part 1 – Chi-Squared Test 00:09:00
Feature Selection - Uni-variate Part 2 - Chi-Squared Test
Feature Selection – Uni-variate Part 2 – Chi-Squared Test 00:10:00
Feature Selection - Recursive Feature Elimination
Feature Selection – Recursive Feature Elimination 00:11:00
Feature Selection - Principal Component Analysis (PCA)
Feature Selection – Principal Component Analysis (PCA) 00:09:00
Feature Selection - Feature Importance
Feature Selection – Feature Importance 00:06:00
Refresher Session - The Mechanism of Re-sampling, Training and Testing
Refresher Session – The Mechanism of Re-sampling, Training and Testing 00:12:00
Algorithm Evaluation Techniques - Introduction
Algorithm Evaluation Techniques – Introduction 00:07:00
Algorithm Evaluation Techniques - Train and Test Set
Algorithm Evaluation Techniques – Train and Test Set 00:11:00
Algorithm Evaluation Techniques - K-Fold Cross Validation
Algorithm Evaluation Techniques – K-Fold Cross Validation 00:09:00
Algorithm Evaluation Techniques - Leave One Out Cross Validation
Algorithm Evaluation Techniques – Leave One Out Cross Validation 00:05:00
Algorithm Evaluation Techniques - Repeated Random Test-Train Splits
Algorithm Evaluation Techniques – Repeated Random Test-Train Splits 00:07:00
Algorithm Evaluation Metrics - Introduction
Algorithm Evaluation Metrics – Introduction 00:09:00
Algorithm Evaluation Metrics - Classification Accuracy
Algorithm Evaluation Metrics – Classification Accuracy 00:08:00
Algorithm Evaluation Metrics - Log Loss
Algorithm Evaluation Metrics – Log Loss 00:03:00
Algorithm Evaluation Metrics - Area Under ROC Curve
Algorithm Evaluation Metrics – Area Under ROC Curve 00:06:00
Algorithm Evaluation Metrics - Confusion Matrix
Algorithm Evaluation Metrics – Confusion Matrix 00:10:00
Algorithm Evaluation Metrics - Classification Report
Algorithm Evaluation Metrics – Classification Report 00:04:00
Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction
Algorithm Evaluation Metrics – Mean Absolute Error – Dataset Introduction 00:06:00
Algorithm Evaluation Metrics - Mean Absolute Error
Algorithm Evaluation Metrics – Mean Absolute Error 00:07:00
Algorithm Evaluation Metrics - Mean Square Error
Algorithm Evaluation Metrics – Mean Square Error 00:03:00
Algorithm Evaluation Metrics - R Squared
Algorithm Evaluation Metrics – R Squared 00:04:00
Classification Algorithm Spot Check - Logistic Regression
Classification Algorithm Spot Check – Logistic Regression 00:12:00
Classification Algorithm Spot Check - Linear Discriminant Analysis
Classification Algorithm Spot Check – Linear Discriminant Analysis 00:04:00
Classification Algorithm Spot Check - K-Nearest Neighbors
Classification Algorithm Spot Check – K-Nearest Neighbors 00:05:00
Classification Algorithm Spot Check - Naive Bayes
Classification Algorithm Spot Check – Naive Bayes 00:04:00
Classification Algorithm Spot Check - CART
Classification Algorithm Spot Check – CART 00:04:00
Classification Algorithm Spot Check - Support Vector Machines
Classification Algorithm Spot Check – Support Vector Machines 00:05:00
Regression Algorithm Spot Check - Linear Regression
Regression Algorithm Spot Check – Linear Regression 00:08:00
Regression Algorithm Spot Check - Ridge Regression
Regression Algorithm Spot Check – Ridge Regression 00:03:00
Regression Algorithm Spot Check - Lasso Linear Regression
Regression Algorithm Spot Check – Lasso Linear Regression 00:03:00
Regression Algorithm Spot Check - Elastic Net Regression
Regression Algorithm Spot Check – Elastic Net Regression 00:02:00
Regression Algorithm Spot Check - K-Nearest Neighbors
Regression Algorithm Spot Check – K-Nearest Neighbors 00:06:00
Regression Algorithm Spot Check - CART
Regression Algorithm Spot Check – CART 00:04:00
Regression Algorithm Spot Check - Support Vector Machines (SVM)
Regression Algorithm Spot Check – Support Vector Machines (SVM) 00:04:00
Compare Algorithms - Part 1 : Choosing the best Machine Learning Model
Compare Algorithms – Part 1 : Choosing the best Machine Learning Model 00:09:00
Compare Algorithms - Part 2 : Choosing the best Machine Learning Model
Compare Algorithms – Part 2 : Choosing the best Machine Learning Model 00:05:00
Pipelines : Data Preparation and Data Modelling
Pipelines : Data Preparation and Data Modelling 00:11:00
Pipelines : Feature Selection and Data Modelling
Pipelines : Feature Selection and Data Modelling 00:10:00
Performance Improvement: Ensembles - Voting
Performance Improvement: Ensembles – Voting 00:07:00
Performance Improvement: Ensembles - Bagging
Performance Improvement: Ensembles – Bagging 00:08:00
Performance Improvement: Ensembles - Boosting
Performance Improvement: Ensembles – Boosting 00:05:00
Performance Improvement: Parameter Tuning using Grid Search
Performance Improvement: Parameter Tuning using Grid Search 00:08:00
Performance Improvement: Parameter Tuning using Random Search
Performance Improvement: Parameter Tuning using Random Search 00:06:00
Export, Save and Load Machine Learning Models : Pickle
Export, Save and Load Machine Learning Models : Pickle 00:10:00
Export, Save and Load Machine Learning Models : Joblib
Export, Save and Load Machine Learning Models : Joblib 00:06:00
Finalizing a Model - Introduction and Steps
Finalizing a Model – Introduction and Steps 00:07:00
Finalizing a Classification Model - The Pima Indian Diabetes Dataset
Finalizing a Classification Model – The Pima Indian Diabetes Dataset 00:07:00
Quick Session: Imbalanced Data Set - Issue Overview and Steps
Quick Session: Imbalanced Data Set – Issue Overview and Steps 00:09:00
Iris Dataset : Finalizing Multi-Class Dataset
Iris Dataset : Finalizing Multi-Class Dataset 00:09:00
Finalizing a Regression Model - The Boston Housing Price Dataset
Finalizing a Regression Model – The Boston Housing Price Dataset 00:08:00
Real-time Predictions: Using the Pima Indian Diabetes Classification Model
Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00
Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00
Real-time Predictions: Using the Boston Housing Regression Model
Real-time Predictions: Using the Boston Housing Regression Model 00:08:00

Frequently Asked Questions

In the UK, the social care system is mainly managed by the local councils. People are directly employed by the councils. They often work together with the health commissioners under joint funding arrangements. Some people work for private companies or voluntary organizations hired by local councils. They help the local councils with their personal social services.

In the UK, the social care system is mainly managed by the local councils. People are directly employed by the councils. They often work together with the health commissioners under joint funding arrangements. Some people work for private companies or voluntary organizations hired by local councils. They help the local councils with their personal social services.

In the UK, the social care system is mainly managed by the local councils. People are directly employed by the councils. They often work together with the health commissioners under joint funding arrangements. Some people work for private companies or voluntary organizations hired by local councils. They help the local councils with their personal social services.

In the UK, the social care system is mainly managed by the local councils. People are directly employed by the councils. They often work together with the health commissioners under joint funding arrangements. Some people work for private companies or voluntary organizations hired by local councils. They help the local councils with their personal social services.

In the UK, the social care system is mainly managed by the local councils. People are directly employed by the councils. They often work together with the health commissioners under joint funding arrangements. Some people work for private companies or voluntary organizations hired by local councils. They help the local councils with their personal social services.

Data Science & Machine Learning with Python
£21
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This course includes:
  • units Number of Units:
    89
  • Lock Access:
    1 Year
  • Duration Duration:
    10 hours, 19 minutes
  • Certificate PDF Certificate
    Included
CPD and SSL Lifetime Access

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