Machine Learning Crash Course

Overview This Machine Learning Crash Course offers a comprehensive introduction to the fundamental concepts of machine learning, followed by practical …

Machine Learning Crash Course

Machine Learning Crash Course

Save Up To 92% - Ends Soon!

Original price was: $417.25.Current price is: $35.30.

TAKE THIS COURSE

Or All courses for £49 (was £499)

Offer Ends In

clender

1 Year Access

teacher

4 Students

durantion

10 hours, 8 minutes

are
are
are
Gift this course
GET THIS COURSE AND 1500+ OTHERS FOR ONLY £49 PER YEAR. FIND OUT MORE

Machine Learning Crash Course Overview

This Machine Learning Crash Course offers a comprehensive introduction to the fundamental concepts of machine learning, followed by practical applications and techniques. Participants will start with the basics of machine learning types and classifications and move through essential Python programming, data handling with Pandas and NumPy, and data visualisation with Matplotlib. The curriculum includes detailed sessions on data pre-processing, feature selection, algorithm evaluation, and model optimization. Finally, participants will gain hands-on experience with real-time predictions using well-known datasets.

Learning Outcomes

  • Understand the core principles and types of machine learning.
  • Prepare systems and environments essential for machine learning projects.
  • Master fundamental Python programming skills relevant to data science.
  • Utilize NumPy for numerical data processing and Matplotlib for data visualization.
  • Perform data manipulation and analysis using Pandas.
  • Learn to handle CSV data files with different Python libraries.
  • Analyse data features through summary statistics and visual plots.
  • Apply various data preprocessing techniques such as normalization and standardization.
  • Evaluate machine learning models using metrics like accuracy, log loss, and AUC-ROC.
  • Implement and compare different machine learning models to identify the most effective one.

Who Is This Course For

This course is designed for individuals who are eager to delve into the field of machine learning, ranging from beginners with basic programming knowledge to intermediate practitioners looking to enhance their skills in data science and machine learning. It is ideal for those who aspire to apply machine learning techniques to real-world problems, including data analysts, software developers, and aspiring data scientists.

 

Eligibility Requirements

Applicants to this course should possess a basic understanding of programming, preferably in Python, and a general familiarity with statistics. While previous experience in data handling and software development is beneficial, it is not mandatory. This course is tailored to accommodate learners from a variety of backgrounds, aiming to equip them with the skills necessary to transition into careers in data science and machine learning.

Entry Requirements

  • Age Requirement: Applicants must be 16 or older, making the course accessible to both young learners and adults.
  • Academic Background: No prior qualifications required, open to all backgrounds.
  • Language Proficiency: A good understanding of English is essential, as all lessons are in English.
  • Numeracy Skills: Basic writing and numeracy skills are needed to follow the course content.

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

Pursuing this course can lead participants down a flourishing career path in the tech industry, particularly in sectors that rely heavily on data analysis and predictive modeling. Successful completion of the course prepares learners for roles that involve building and optimising machine learning models, making them valuable assets in industries such as finance, healthcare, retail, and technology. Advancements may include leadership positions in data science teams or specialised roles in machine learning engineering.

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
Resources-Machine Learning Crash Course 00:00:00
Algorithm Evaluation Metrics - Mean Absolute Error
Algorithm Evaluation Metrics – Mean Absolute Error – Dataset Introduction 00:06:00
Algorithm Evaluation Metrics - Mean Square Error
Algorithm Evaluation Metrics – Mean Square Error 00:03:00
Algorithm Evaluation Metrics - R Squared
Classification Algorithm Spot Check – Logistic Regression 00:12:00
Classification Algorithm Spot Check - Logistic Regression
Classification Algorithm Spot Check – Linear Discriminant Analysis 00:04:00
Classification Algorithm Spot Check - Linear Discriminant Analysis
Classification Algorithm Spot Check – K-Nearest Neighbors 00:05:00
Classification Algorithm Spot Check - K-Nearest Neighbors
Classification Algorithm Spot Check – Naive Bayes 00:04:00
Classification Algorithm Spot Check - Naive Bayes
Classification Algorithm Spot Check – CART 00:04:00
Classification Algorithm Spot Check - CART
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-Machine Learning Crash Course 00:00:00

How Do Our Courses Work?

Purchase and payment

Secure your course with an easy one-time payment and get instant access.

Course access

Enjoy 1-year unlimited access to study at your own pace, anytime, anywhere.

Certificate

Complete the course and order your accredited certificate to showcase your achievement.

Continued support

Get 24/7 expert support to assist you throughout your learning journey.

Dive into an enriching online learning journey with Alpha Academy. We pride ourselves on offering a diverse range of courses tailored to your needs. Elevate your expertise or discover a new passion. With Alpha Academy, your pursuit of knowledge has no bounds.

Contact

For Business

Certificate validator

Payment methods possible

© ALPHA ACADEMY IS A PART OF ADAMS ACADEMY INC. LTD.

top
0
    0
    Your Cart
    Your cart is emptyReturn to Shop

    EARLY SPRING SALE – Get 2500+ COURSES FOR Original price was: $652.69.Current price is: $64.09. / year

    ADD OFFER TO CART-

    No more than 50 active courses at any one time. Membership renews after 12 months. Cancel anytime from your account. Certain courses are not included. Can't be used in conjunction with any other offer.

      Apply Coupon
        ×