All Courses for £49
Overview
Is Data Science & Machine Learning with Python your next career move? Are you aware that 90% of companies are integrating data science and machine learning into their operations? This online course offers you the chance to dive into one of the fastest-growing fields. Did you know the job market for data scientists is expected to grow by 28% by 2026? This Data Science & Machine Learning with Python course fits into the larger industry trend of data-driven decision-making and addresses the need for theoretical knowledge in machine learning. The Data Science & Machine Learning with Python course will help you develop the skills necessary to understand complex data and prepare for a promising career in data science.
Description
Data Science & Machine Learning with Python is designed to provide a comprehensive understanding of machine learning concepts and applications. The Data Science & Machine Learning with Python course covers everything from the basics of machine learning, including definitions and classifications, to advanced topics like algorithm evaluation and model finalization. You’ll start with the foundational concepts and gradually build up to more complex topics, ensuring a thorough grasp of the subject.
In the initial modules, you’ll learn about system and environment preparation, followed by assignments and exercises to reinforce your understanding of Python basics. As you progress, you’ll delve into data handling with NumPy and data visualization using Matplotlib and Pandas. These modules will equip you with the theoretical knowledge needed to handle and analyze large datasets efficiently.
The final modules focus on advanced topics such as feature selection, algorithm evaluation, and performance improvement techniques. You’ll explore various machine learning models and learn how to compare them to choose the best one for your needs. The Data Science & Machine Learning with Python course culminates in learning how to finalize models and make real-time predictions, preparing you for a successful career in data science.
Learning Outcomes:
- Understand the basic concepts and definitions of machine learning.
- Learn the different types of machine learning classifications and applications.
- Gain proficiency in Python basics, including functions and data structures.
- Explore the essentials of NumPy for numerical data handling.
- Master data visualization techniques using Matplotlib and Pandas.
- Understand and apply various algorithm evaluation metrics and techniques.
Begin your data science journey with our comprehensive Data Science & Machine Learning with Python course today!
Why Choose Us?
- This course is accredited by the CPD Quality Standards.
- Lifetime access to the whole collection of the learning materials.
- Online test with immediate results.
- Enroling in the course has no additional cost.
- You can study and complete the course at your own pace.
- Study for the course using any internet-connected device, such as a computer, tablet, or mobile device.
Certificate of Achievement
Upon successful completion, you will qualify for the UK and internationally-recognised CPD certificate and you can choose to make your achievement formal by obtaining your PDF Certificate at a cost of £4.99 and Hardcopy Certificate for £9.99.
Who Is This Course For?
- Individuals looking to start a career in data science.
- Professionals aiming to transition into the field of machine learning.
- Students interested in understanding the theoretical aspects of data analysis.
- Data analysts want to enhance their knowledge of machine learning.
- Researchers seeking to apply machine learning concepts to their work.
- Anyone with a passion for data and analytics.
Requirements
The Data Science & Machine Learning with Python course requires no prior degree or experience. All you require is English proficiency, numeracy literacy and a gadget with stable internet connection. Learn and train for a prosperous career in the thriving and fast-growing industry of Data Science & Machine Learning with Python, without any fuss.
Career Path
After completing Data Science & Machine Learning with Python course, you should be able to pursue the following career pathways:
- Data Scientist
- Machine Learning Engineer
- Data Analyst
- Research Scientist
- Business Intelligence Analyst
- Statistician
- Data Engineer
Order Your Certificate
To order CPD Quality Standard Certificate, we kindly invite you to visit the following link:
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 | ||
| Order Your Certificate | |||
| Order Your Certificate | 00:00:00 | ||
Related Courses
