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Data Science & Machine Learning with Python

Data Science & Machine Learning with Python

  • 30 Day Money Back Guarantee
  • Completion Certificate
  • 24/7 Technical Support

Highlights

  • On-Demand course

  • 10 hours 19 minutes

  • Intermediate level

Description

Overview of Data Science & Machine Learning with Python

Join our Data Science & 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 & 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 & 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!

This Data Science & Machine Learning with Python Course will help you to learn:
  • Learn strategies to boost your workplace efficiency.

  • Hone your skills to help you advance your career.

  • Acquire a comprehensive understanding of various topics and tips.

  • Learn in-demand skills that are in high demand among UK employers

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 & Machine Learning with Python. Enrol today and complete the course to achieve a certificate that can change your career forever.

Details
Perks of Learning with IOMH
  • One-To-One Support from a Dedicated Tutor Throughout Your Course.

  • Study Online - Whenever and Wherever You Want.

  • Instant Digital/ PDF Certificate.

  • 100% Money Back Guarantee.

  • 12 Months Access.

Process of Evaluation

After studying the 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

Certificate of Completion - Digital / PDF Certificate

After completing the Data Science & Machine Learning with Python course, you can order your CPD Accredited Digital / PDF Certificate for £5.99. 

Certificate of Completion - Hard copy Certificate

You can get the CPD Accredited Hard Copy Certificate for £12.99.

Shipping Charges:

  • Inside the UK: £3.99

  • International: £10.99

Who Is This Course for?

This Data Science & 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 qualification or experience to enrol in the Data Science & 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 & 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.

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

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