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DesignSee the Curriculum Section for materials and enjoy learning with Imperial Academy.
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 | ||
| Resources | |||
| Resources – Data Science & Machine Learning with Python | 00:00:00 | ||
Quality Licence Scheme Endorsed Certificate of Achievement
Learners will get an endorsed certificate of achievement (included with the course) directly at their doorstep after successfully completing the package! It should be noted that international students must pay a £10 shipping cost.
For ordering additional QLS Certificates, other than this course, the fees will be following with £10 for international shipping only
- Level 1 - £109
- Level 2 - £115
- Level 3 - £119
- Level 4 - £125
- Level 5 - £129
- Level 6 - £135
- Level 7 - £139
CPD Accredited Certification
Upon successfully completing each of the courses in this package, you will
be qualified for CPD Accredited Certificates. Certification is available –
- PDF completely free of charge
- Hard copy for £3.99 each
It should also be noted that international students must pay a £10 shipping cost.
Endorsement
The Quality Licence Scheme has endorsed this course for its high-quality, non-regulated provision and training programmes. This course is not regulated by Ofqual and is not an accredited qualification. Imperial Academy will be able to advise you on any further recognition, for example, progression routes into further and/or higher education.
Imperial Academy is an approved reselling partner for Quality Licence Scheme courses under One Education
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