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Course overview Learn about Employment Law quickly and enhance your skills. You will understand Employment Law and its key areas, covering the …
Learn about Python Machine Learning & Data Science Fundamentals quickly and enhance your skills. You will understand Python Machine Learning & Data Science Fundamentals and its key areas, covering the fundamentals and beyond. The Python Machine Learning & Data Science Fundamentals course is divided into small modules that are easy to grasp even by complete beginners. Plus, the Python Machine Learning & Data Science Fundamentals course comes with tutor support, so if you have any questions related to Python Machine Learning & Data Science Fundamentals, you can ask them via live chat or email.
Completing this Python Machine Learning & Data Science Fundamentals course will give you the confidence to stand out to prospective employers in the Python Machine Learning & Data Science Fundamentals industry & other relevant sectors, climb up the career ladder and boost your earning potential in no time.
Enrol in the Python Machine Learning & Data Science Fundamentals course today and invest in your bright future.
Anyone with an interest in Python Machine Learning & Data Science Fundamentals will find this course valuable. Whether you are a student/ aspiring professional aiming to explore your opportunities in Python Machine Learning & Data Science Fundamentals sectors, then this Python Machine Learning & Data Science Fundamentals course might just be the perfect opportunity for you.
After successfully completing the Python Machine Learning & Data Science Fundamentals course, you can apply for a CPD accredited course in both digital & physical format.
The pricing for the Python Machine Learning & Data Science Fundamentals course completion certificate is as follows:
Complete your Python Machine Learning & Data Science Fundamentals course successfully and develop yourself to enhance your chances of succeeding in Python Machine Learning & Data Science Fundamentals sectors and other relevant fields.
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 – Mean Absolute Error – Dataset Introduction | 00:06:00 | ||
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 - 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 | ||
Classification Algorithm Spot Check - Naive Bayes | |||
Classification Algorithm Spot Check – Naive Bayes | 00:04:00 | ||
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 |
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