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Data Science and Machine Learning with Python

Data Science and Machine Learning with Python

Curious about the future of AI? Start your journey here with our comprehensive Data Science and Machine Learning with Python course. Enrol now!

Enrolled Students: 78

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Overview of Data Science and Machine Learning with Python

Join our Data Science and Machine Learning with Python course and discover your hidden skills, setting you on a path to success in this area. Get ready to improve your skills and achieve your biggest goals. The Data Science and Machine Learning with Python course has everything you need to get a great start in this sector.

Improving and moving forward is key to getting ahead personally. The Data Science and Machine Learning with Python course is designed to teach you the important stuff quickly and well, helping you to get off to a great start in the field. So, what are you looking for? Enrol now!

Get a Quick Look at The Course Content:

This Data Science and Machine Learning with Python Course will help you to learn:

This course covers the topic you must know to stand against the tough competition. The future is truly yours to seize with this Data Science and Machine Learning with Python. Enrol today and complete the course to achieve a certificate that can change your career forever.

Course Description

Perks of Learning with IOMH

Process of Evaluation

After studying the Data Science and Machine Learning with Python course, an MCQ exam or assignment will test your skills and knowledge. You have to get a score of 60% to pass the test and get your certificate.

Certificate of Achievement

Upon successfully completing the final assessment, you’ll unlock a CPD-accredited Certificate of Achievement—a valuable credential that demonstrates your commitment to professional development. The PDF Certificate is priced at £8.99 and will be emailed to you immediately. Also, A printed Hard copy Certificate can be purchased for £15.99 and will be sent to your address by post.

Who Is This Course for?

This Data Science and Machine Learning with Python is suitable for anyone aspiring to start a career in relevant field; even if you are new to this and have no prior knowledge, this course is going to be very easy for you to understand.

On the other hand, if you are already working in this sector, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level.

This course has been developed with maximum flexibility and accessibility, making it ideal for people who don’t have the time to devote to traditional education.

Requirements

You don’t need any educational qualifications or experience to enrol in the Data Science and Machine Learning with Python course.

Do note: You must be at least 16 years old to enrol.

Any internet-connected device, such as a computer, tablet, or smartphone, can access this online course.

Career Path

The certification and skills you get from this Data Science and Machine Learning with Python Course can help you advance your career and gain expertise in several fields, allowing you to apply for high-paying jobs in related sectors.

Frequently Asked Questions

Q. How do I purchase a course?

1. You need to find the right course on our IOMH website at first. You can search for any course or find the course from the Courses section of our website.
2. Click on Take This Course button, and you will be directed to the Cart page.
3. You can update the course quantity and also remove any unwanted items in the CART and after that click on the Checkout option and enter your billing details.
4. Once the payment is made, you will receive an email with the login credentials, and you can start learning after logging into the portal.

Q. I have purchased the course when will I be able to access the materials?

After purchasing the course, you should receive an email with the login credentials within 24 hours. Please check your spam or junk folder if you didn’t receive it in your inbox. You can access your courses by logging into your account. If you still need any assistance, please get in touch with our Customer Support team by providing the details of your purchase.

Q. I haven't received my certificate yet. What should I do?

You should receive your Digital Certificate within 24 hours after placing the order, and it will take 3-9 days to deliver the hard copies to your address if you are in the UK.

For International Delivery, it will take 20-25 days.

If you require any assistance, get in touch with our dedicated Customer Support team, and your queries/issues will be dealt with accordingly.

Q. I don't have a credit/debit card, what other methods of payment do you accept?

You can make the payment using PayPal or you can Bank Transfer the amount.

For Bank transfer you will require an invoice from us and you need to contact our Customer Support team and provide details of your purchase to get the invoice.

After that, you will receive an email with the invoice and bank details and you can make the payment accordingly.

Q. Can I do the courses from outside UK?

We are an online course provider, and learners from anywhere in the world can enrol on our courses using an internet-connected device.

Q. When I log into the account it says 'Contact Administrator'.

To resolve this issue, please log out of your account and then log back in.

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 and Machine Learning with Python	00:00:00