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Understand the Basics of Machine Learning

Overview An extensive introduction to the principles of machine learning is given in this course.  We’ll go over the fundamental …

Understand the Basics of Machine Learning

Understand the Basics of Machine Learning

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Understand the Basics of Machine Learning Overview

An extensive introduction to the principles of machine learning is given in this course.  We’ll go over the fundamental ideas, several varieties of machine learning, and applications across numerous industries. Python programming and working with tools for data manipulation and visualisation such as NumPy, Pandas, and Matplotlib will provide you with practical experience.  The course covers feature selection strategies, data preparation techniques, and common machine learning algorithms for tasks involving regression and classification. We’ll go over how to assess these algorithms and determine which model is ideal for your undertaking. Lastly, you will discover how to store and use your models for in-the-moment forecasting.

Learning Outcomes

  • Explain machine learning terms and the differences between the different kinds of machine learning.
  • Describe the practical uses of machine learning.
  • Create a Python programming development environment.
  • Use Python tools for data manipulation and visualisation, such as NumPy, Pandas, and Matplotlib.
  • Data should be cleaned and ready for machine learning tasks.
  • Use feature selection strategies to enhance model functionality.
  • Differentiate between the applications of regression and classification algorithms.
  • Analyse how well various machine learning models perform.
  • Use model-improvement strategies like parameter tuning and ensembling.
  • Save and use machine learning models to make predictions instantly.

Who Is This Course For

This course is intended for people who want to learn the fundamentals of machine learning but have little to no prior experience in the field. It can be used to solve issues in a variety of fields and is appropriate for people who are interested in the ways that machines learn.  This course gives you the fundamental knowledge and abilities to start your machine learning journey, regardless of whether you’re a student, professional looking to advance in your career, or just have a passion for technology.

Entry Requirements

  • Age Requirement: Applicants must be aged 16 or above, allowing both young learners and adults to engage in this educational pursuit.
  • Academic Background: There are no specific educational prerequisites, opening the door to individuals from diverse academic histories.
  • Language Proficiency: A good command of the English language is essential for comprehension and engagement with the course materials.
  • Numeracy Skills: Basic numeracy skills are required for understanding nutritional data and dietary planning.

Why Choose Us

  • Affordable, engaging & high-quality e-learning study materials;
  • Tutorial videos/materials from the industry-leading experts;
  • Study in a user-friendly, advanced online learning platform;
  • Efficient exam systems for the assessment and instant result;
  • The UK & internationally recognised accredited
  • Access to course content on mobile, tablet or desktop from anywhere, anytime;
  • The benefit of career advancement opportunities;
  • 24/7 student support via email.

Career Path

Machine learning expertise opens doors to a multitude of exciting career paths. You could become a Machine Learning Engineer, building and deploying machine learning models for real-world applications. Data Scientists leverage their machine learning skills to extract insights from large datasets.  Alternatively, you might pursue a role as a Machine Learning Researcher, developing new algorithms and pushing the boundaries of the field.  The possibilities are vast, and this course provides a solid foundation for a fulfilling career in machine learning.

 

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 2
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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