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Data Science & Machine Learning with Python

14 STUDENTS ENROLLED

Overview

The Data Science & Machine Learning with Python is the best way for you to gain deep insight and knowledge of this topic. You will learn from industry experts and obtain a professional certificate after completing the course. Enrol now for a limited-time discounted price.

Like all the courses of One Education, this Data Science & Machine Learning with Python is designed with the utmost attention and thorough research. All the topics are broken down into easy to understand bite-sized modules that help our learners to understand each lesson very easily.

We don’t just provide courses at One Education; we provide a rich learning experience. After purchasing a course from One Education, you get complete 1-year access with tutor support.

Our expert instructors are always available to answer all your questions and make your learning experience exquisite.

After completing the Data Science & Machine Learning with Python, you will instantly get an e-certificate that will help you get jobs in the relevant field and will enrich your CV.

If you want to learn about this topic and want to get professional qualifications, simply enrol this Data Science & Machine Learning with Python from One Education.

Why People Love and Enrol in The Data Science & Machine Learning with Python from One Education

  • Instantly accessible CPD-accredited certificate on successful completion of this Data Science & Machine Learning with Python
  • 24/7 access to the course for 12 months
  • Study at your own pace
  • No hidden fees or exam charges
  • Full Tutor support on weekdays (Monday – Friday)
  • Efficient assessment and instant results

Course design

The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace.

You are taught through a combination of

  • Video lessons
  • Online study materials

How is the course assessed?

To successfully complete the course you must pass an automated, multiple-choice assessment. The assessment is delivered through our online learning platform. You will receive the results of your assessment immediately upon completion.

Will I receive a certificate of completion?

Upon successful completion, you will qualify for the UK and internationally-recognized professional qualification and you can choose to make your achievement formal by obtaining your PDF Certificate at a cost of £9 and Hard Copy Certificate for £15.

Why study this course

Whether you’re an existing practitioner or aspiring professional, this course will enhance your expertise and boost your CV with key skills and an accredited qualification attesting to your knowledge.

The Data Science & Machine Learning with Python is open to all, with no formal entry requirements. All you need is a passion for learning, a good understanding of the English language, numeracy and IT, and to be over the age of 16.

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 – Train the Trainer Coach: Complete Guide to Coaching Trainers 00:00:00

Course Reviews

4.7

4.7
10 ratings
  • 5 stars7
  • 4 stars3
  • 3 stars0
  • 2 stars0
  • 1 stars0
  1. Phoebe Hale

    5

    The whole session was a blast for me. I learned a lot and intend to enrol in more of their courses.

  2. Oliver Pratt

    5

    This course is perfect. It feels like it has been made for me. It discussed all the topics that I was struggling to understand and helped me to start my programming journey with Python. A five-start course.

  3. Oliver Pratt

    5

    This course is a great source to learn about the fundamentals of Python programming and data science. The course contents are very easy and the bite-sized lessons will help you to digest the new information effortlessly.

  4. Freddie Wade

    4

    One of the best courses I’ve ever taken on learning Python machine learning, and data science. The modules were jam-packed with helpful tips and facts. The course was fantastic.

  5. Sophie Gibbs

    5

    I enjoyed doing this course. The lectures are concise yet very informative. I highly recommend this course.

  6. 1 2
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