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10 hours 19 minutes
Intermediate level
Join our Data Science & Machine Learning with Python course and discover your hidden skills, setting you on a path to success in this area. Get ready to improve your skills and achieve your biggest goals. The Data Science & Machine Learning with Python course has everything you need to get a great start in this sector.
Improving and moving forward is key to getting ahead personally. The Data Science & Machine Learning with Python course is designed to teach you the important stuff quickly and well, helping you to get off to a great start in the field. So, what are you looking for? Enrol now!
Learn strategies to boost your workplace efficiency.
Hone your skills to help you advance your career.
Acquire a comprehensive understanding of various topics and tips.
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This course covers the topic you must know to stand against the tough competition. The future is truly yours to seize with this Data Science & Machine Learning with Python. Enrol today and complete the course to achieve a certificate that can change your career forever.
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After studying the course, an MCQ exam or assignment will test your skills and knowledge. You have to get a score of 60% to pass the test and get your certificate.
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After completing the Data Science & Machine Learning with Python course, you can order your CPD Accredited Digital / PDF Certificate for £5.99.
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This Data Science & Machine Learning with Python is suitable for anyone aspiring to start a career in relevant field; even if you are new to this and have no prior knowledge, this course is going to be very easy for you to understand.
On the other hand, if you are already working in this sector, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level.
This course has been developed with maximum flexibility and accessibility, making it ideal for people who don't have the time to devote to traditional education.
You don't need any educational qualification or experience to enrol in the Data Science & Machine Learning with Python course.
Do note: you must be at least 16 years old to enrol.
Any internet-connected device, such as a computer, tablet, or smartphone, can access this online course.
The certification and skills you get from this Data Science & Machine Learning with Python Course can help you advance your career and gain expertise in several fields, allowing you to apply for high-paying jobs in related sectors.
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 | |
Resources | ||
Resources - Data Science & Machine Learning with Python | 00:00:00 |