- Home
- Course
- IT & Software
- Complete Python Machine Learning & Data Science Fundamentals

- Level: Intermediate
- Modules: 90 Modules
- Duration: 10 hours, 29 minutes
- Students: 0

The “Complete Python Machine Learning & Data Science Fundamentals” course covers the foundational concepts of machine learning, data science, and Python programming. It includes hands-on exercises, data visualization, algorithm evaluation techniques, feature selection, and performance improvement using ensembles and parameter tuning.

- Understand the fundamental concepts and types of machine learning, data science, and Python programming.
- Learn to prepare the system and environment for data analysis and machine learning tasks.
- Master the basics of Python, NumPy, Matplotlib, and Pandas for data manipulation and visualization.
- Gain insights into dataset summary statistics, data visualization techniques, and data preprocessing.
- Explore feature selection methods and evaluation metrics for classification and regression algorithms.
- Compare and select the best machine learning model using pipelines and ensembles.
- Learn to export, save, load machine learning models, and finalize the chosen models for real-time predictions.

**Why buy this Complete Python Machine Learning & Data Science Fundamentals?**

- Unlimited access to the course for forever
**Digital Certificate, Transcript, student ID**all included in the price- Absolutely no hidden fees
- Directly receive
**CPD**accredited qualifications after course completion - Receive one to one assistance on every weekday from professionals
- Immediately receive the PDF certificate after passing
- Receive the original copies of your certificate and transcript on the next working day
- Easily learn the skills and knowledge from the comfort of your home

After studying the course materials of the **Complete Python Machine Learning & Data Science Fundamentals** there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60.

This **Complete Python Machine Learning & Data Science Fundamentals** course is ideal for

- Students
- Recent graduates
- Job Seekers
- Anyone interested in this topic
- People already working in the relevant fields and want to polish their knowledge and skill.

This **Complete Python Machine Learning & Data Science Fundamentals** does not require you to have any prior qualifications or experience. You can just enrol and start learning.This **Complete Python Machine Learning & Data Science Fundamentals** was made by professionals and it is compatible with all PC’s, Mac’s, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.

As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This **Complete Python Machine Learning & Data Science Fundamentals **is a great way for you to gain multiple skills from the comfort of your home.

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 | 00:08:00 | ||

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 | |||

Understanding the CSV data file | 00:09:00 | ||

Load and Read CSV data file using NumPy | |||

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

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 | 00:07:00 | ||

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 | ||

Resources | |||

Resources – Python Machine Learning & Data Science Fundamentals | 00:00:00 |

★
★
★
★
★

★
★
★
★
★

Embark on a transformative journey into the realm of cutting-edge development with our course, “Master Spring Boot 3.0 and MongoDB.” …

$21 ~~$329.00~~

1

★
★
★
★
★

★
★
★
★
★

Embark on a transformative learning journey with our course, “Microsoft Teams – Master Microsoft Teams.” Unleash the full potential of …

$21 ~~$329.00~~

0

★
★
★
★
★

★
★
★
★
★

Embark on a transformative journey into the realm of app creation with our course, “Master the Basics of Microsoft Power …

$21 ~~$329.00~~

1