Data Science & Machine Learning with Python
Data Science & Machine Learning with Python
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Intermediate
79 Students -
10 hours, 19 minutes
Learning Outcome
- Know how to prepare the system and environment.
- Learn the basics of Python.
- Gain an in-depth understanding of Machine Learning.
- Understand data structures.
- Familiarise yourself with NumPy, and more.
Data Science and Machine Learning with Python Overview
The Data Science and Machine Learning with Python course is the best way for you to gain deep insight and knowledge of this topic. This course will assist you in building your expertise in this area and boost your career.
Like all the courses of One Education, this Data Science and Machine Learning with Python course is designed with the utmost attention and thorough research. All the topics are broken down into easy to understand bite-sized modules that help our learners to understand each lesson very easily.
We don’t just provide courses at One Education; we provide a rich learning experience. After purchasing a course from One Education, you get complete 1-year access with tutor support.
In addition, our expert instructors are always available to answer all your questions and make your learning experience exquisite. After completing the Data Science and Machine Learning with Python course, you will instantly get an e-certificate that will help you get jobs in the relevant field and will enrich your CV.
If you want to learn about this topic and want to develop your skills, simply enrol on this Data Science and Machine Learning with Python course from One Education.
Why People Love and Enrol in The Data Science and Machine Learning with Python from One Education
- Instantly accessible CPD-accredited certificate on successful completion of this Data Science & Machine Learning with Python
- 24/7 access to the course for 12 months
- Study at your own pace
- No hidden fees or exam charges
- Full Tutor support on weekdays (Monday – Friday)
- Efficient assessment and instant results
Course design
The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace.
You are taught through a combination of
- Video lessons
- Online study materials
Sneak Peek-Data Science and Machine Learning with Python
Free resource – Best Way to Learn Python: Step-by-Step Guide for Beginners
How is the course assessed?
To successfully complete the course you must pass an automated, multiple-choice assessment. The assessment is delivered through our online learning platform. You will receive the results of your assessment immediately upon completion.
Will I receive a certificate of completion?
Upon successful completion, you will qualify for the UK and internationally-recognized professional qualification and you can choose to make your achievement formal by obtaining your PDF Certificate at a cost of £9 and Hard Copy Certificate for £15.
Why study this Data Science and Machine Learning course
Whether you’re an existing practitioner or aspiring professional, this course will enhance your expertise and boost your CV with key skills and an accredited qualification attesting to your knowledge.
The Data Science & Machine Learning with Python is open to all, with no formal entry requirements. All you need is a passion for learning, a good understanding of the English language, numeracy and IT, and to be over the age of 16.
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 | ||
| Order Your Certificate | |||
| Order Your Certificate Now | 00:00:00 | ||
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