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Data Science & Machine Learning with Python
950 4.7 (7 Reviews)
In this comprehensive course, you will receive detailed information about Python programming language. The course will deliver elaborate lessons on NumPy and Pandas. Furthermore, while progressing with the study you will get to learn how to do data analysis, and visualisation using Python.
A survey comes with result that, over 78% of data scientists, data analysts and software engineers use Python over any other programming language for their work. So, if you want to launch your career in any of these fields, how can you shine without having expert knowledge of Python? We formulated this Data Science & Machine Learning With Python course to give you a thorough understanding of this matter.
In this comprehensive course, you will receive detailed information about Python programming language. The course will deliver elaborate lessons on NumPy and Pandas. Furthermore, while progressing with the study you will get to learn how to do data analysis, and visualisation using Python. Along with that, the course will give you a clear picture of algorithm evaluation techniques, principal component analysis and much more.
Upon the successful completion of this course, you will get a QLS- Endorsed certificate of achievement, which can help you grab the attention of employers. So, what are you waiting for? Join us now to begin your learning journey.
Learning Outcomes
- Learn Python for data analysis using NumPy and Pandas
- Obtain a clear understanding of datasets visualisation
- Become more proficient in principal component analysis
- Gain knowledge about algorithm evaluation techniques
- Improve your knowledge of performance improvement
- Equip yourself with the skills for data preparations and data modelling
Why choose this course
- Affordable, Accessible, informative and well-structured study materials
- Earn a globally recognised accredited qualification
- Study in your own time, at your own pace, through any device
- Benefit from feedback through mock exams and multiple-choice assessments
- Excellent career advancement/career switch opportunities
- Get 24/7 help or advice from our email and live chat teams
Entry requirements
This course is available to learners of all academic backgrounds, but you must be 16 or above to undertake the qualification.
Apart from that, all you need is an internet connection, a good understanding of English and a passion for learning.
Sneak Peek
Certification
Endorsed Certificate of Achievement from the Quality Licence Scheme
Upon successful completion of the final assessment, you will be eligible to apply for the Quality Licence Scheme Endorsed Certificate of achievement. This certificate will be delivered to your doorstep through the post for £119. An extra £10 postage charge will be required for students leaving overseas.
CPD Accredited Certificate
After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post.
Method of Assessment
At the end of the course, there will be a final assessment. A set of questions will be provided, and you can complete these questions according to your convenient time. After you submit the assignment, our expert team will evaluate them and provide constructive feedback.
Career path
We designed this course not only for improving your knowledge of Python but also to prepare you for job opportunities. Some of them are given in the down below –
- Web Developer
- Software Engineer
- Data Scientist
- Machine Learning Engineer
- Data Analyst
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 | 00:10:00 | ||
| Learn Basics of python - Assignment | |||
| Learn Basics of python – Assignment | 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 - 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 | ||
| Assignment | |||
| Assignment – Data Science & Machine Learning with Python | 3 weeks, 3 days | ||
| Order your Certificates & Transcripts | |||
| Order your Certificates & Transcripts | 00:00:00 | ||
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Frequently asked questions
Can’t find the anwser you’re looking for ? Reach out to customer support team.
There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course.
You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience.
For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime.
Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks.
We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase.
Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course.
If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.