Complete Python Machine Learning & Data Science Fundamentals

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Complete Python Machine Learning & Data Science Fundamentals

Complete Python Machine Learning & Data Science Fundamentals

Level: Intermediate
Modules: 90 Modules
Duration: 10 hours, 29 minutes
Students: 3

The “Complete Python Machine Learning & Data Science Fundamentals” course covers the foundational concepts of machine learning, data science, and Python programming. It includes hands-on exercises, data visualization, algorithm evaluation techniques, feature selection, and performance improvement using ensembles and parameter tuning.

Learning Outcomes:

Understand the fundamental concepts and types of machine learning, data science, and Python programming.
Learn to prepare the system and environment for data analysis and machine learning tasks.
Master the basics of Python, NumPy, Matplotlib, and Pandas for data manipulation and visualization.
Gain insights into dataset summary statistics, data visualization techniques, and data preprocessing.
Explore feature selection methods and evaluation metrics for classification and regression algorithms.
Compare and select the best machine learning model using pipelines and ensembles.
Learn to export, save, load machine learning models, and finalize the chosen models for real-time predictions.

Why buy this Complete Python Machine Learning & Data Science Fundamentals?

Unlimited access to the course for forever
Digital Certificate, Transcript, student ID all included in the price
Absolutely no hidden fees
Directly receive CPD accredited qualifications after course completion
Receive one to one assistance on every weekday from professionals
Immediately receive the PDF certificate after passing
Receive the original copies of your certificate and transcript on the next working day
Easily learn the skills and knowledge from the comfort of your home

Certification

After studying the course materials of the Complete Python Machine Learning & Data Science Fundamentals there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60.

Who is this course for?

This Complete Python Machine Learning & Data Science Fundamentals course is ideal for

Students
Recent graduates
Job Seekers
Anyone interested in this topic
People already working in the relevant fields and want to polish their knowledge and skill.

Prerequisites

This Complete Python Machine Learning & Data Science Fundamentals does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Complete Python Machine Learning & Data Science Fundamentals was made by professionals and it is compatible with all PC’s, Mac’s, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.

Career path

As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Complete Python Machine Learning & Data Science Fundamentals is a great way for you to gain multiple skills from the comfort of your home.

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	00:08:00
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
	Understanding the CSV data file	00:09:00
Load and Read CSV data file using NumPy
	Load and Read CSV data file using Python Standard Library	00:09:00
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	00:07:00
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 – Python Machine Learning & Data Science Fundamentals	00:00:00