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Python Machine Learning & Data Science Fundamentals

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Course overview Learn about Employment Law quickly and enhance your skills. You will understand Employment Law and its key areas, covering the …

Course overview

Learn about Python Machine Learning & Data Science Fundamentals quickly and enhance your skills. You will understand Python Machine Learning & Data Science Fundamentals and its key areas, covering the fundamentals and beyond. The Python Machine Learning & Data Science Fundamentals course is divided into small modules that are easy to grasp even by complete beginners. Plus, the Python Machine Learning & Data Science Fundamentals course comes with tutor support, so if you have any questions related to Python Machine Learning & Data Science Fundamentals, you can ask them via live chat or email.

Completing this Python Machine Learning & Data Science Fundamentals course will give you the confidence to stand out to prospective employers in the Python Machine Learning & Data Science Fundamentals industry & other relevant sectors, climb up the career ladder and boost your earning potential in no time.

Enrol in the Python Machine Learning & Data Science Fundamentals course today and invest in your bright future.

What's inside

Who is this course for?

Anyone with an interest in Python Machine Learning & Data Science Fundamentals will find this course valuable. Whether you are a student/ aspiring professional aiming to explore your opportunities in Python Machine Learning & Data Science Fundamentals sectors, then this Python Machine Learning & Data Science Fundamentals course might just be the perfect opportunity for you.

Certification

After successfully completing the Python Machine Learning & Data Science Fundamentals course, you can apply for a CPD accredited course in both digital & physical format.

The pricing for the Python Machine Learning & Data Science Fundamentals course completion certificate is as follows:

Learning outcomes

Explore a diverse range of aspects of this field after the successful completion of the Python Machine Learning & Data Science Fundamentals course
Get inside-out knowledge on Python Machine Learning & Data Science Fundamentals topic from the industry experts
Acquire skills that will help you to explore many opportunities

Career path

Complete your Python Machine Learning & Data Science Fundamentals course successfully and develop yourself to enhance your chances of succeeding in Python Machine Learning & Data Science Fundamentals sectors and other relevant fields.

Course Curriculum

90 Modules 10 hours, 16 minutes

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 – Mean Absolute Error – Dataset Introduction	00:06:00
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 - 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
Classification Algorithm Spot Check - Naive Bayes
	Classification Algorithm Spot Check – Naive Bayes	00:04:00
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

Certificate of Achievement from Institute of Beauty & Makeup

A Certificate of Achievement from the Institute of Beauty & Makeup can be a valuable asset for individuals pursuing a career in the relevent industry. This certification signifies that the recipient has successfully completed a specialized program or course from Institute of Beauty & Makeup.

It can offer a range of benefits for their future and job prospects in relevent sector.

Professional Credibility
Skill Development
Competitive Edge
Increased Job Opportunities
Entrepreneurial Opportunities
Personal Growth & Networking Opportunities

Certificate of Achievement from CPD Quality Standard

Professionalism and self-improvement are clearly priorities for CPD. Through continuing professional development, an individual might recognize knowledge gaps and fill them in using a recognizable improvement strategy.

Recognition: Having CPD QS Accreditation increases the value of your professional accomplishments and is recognized internationally.
CPD Hours/Points: Your commitment to lifelong learning is demonstrated by the amount of CPD hours or points that are included on each certificate.
Free Course Transcript: Get a thorough transcript of your course that lists all of your accomplishments and the modules you've finished.
Module Breakdown: A breakdown of the course modules you have successfully completed will help you better understand your progress.
Skill Enhancement: Access up-to-date, relevant content that's tailored to your industry, ensuring you're always at the forefront of industry trends.
Career Advancement: A CPD QS Accredited Certificate can open doors to new opportunities and advancement within your field.