Complete Python Machine Learning & Data Science Fundamentals

Complete Python Machine Learning & Data Science Fundamentals

0 STUDENTS ENROLLED

    Overview

    The Complete Python Machine Learning & Data Science Fundamentals 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 Complete Python Machine Learning & Data Science Fundamentals 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 Complete Python Machine Learning & Data Science Fundamentals, 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 Complete Python Machine Learning & Data Science Fundamentals from One Education.

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

    • Instantly accessible CPD-accredited certificate on successful completion of this Complete Python Machine Learning & Data Science Fundamentals
    • 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 Complete Python Machine Learning & Data Science Fundamentals 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 00:10:00
    Learn Basics of python - Assignment
    Learn Basics of python – Assignment 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

    Course Reviews

    N.A

    0
    0 ratings
    • 5 stars0
    • 4 stars0
    • 3 stars0
    • 2 stars0
    • 1 stars0

    No Reviews found for this course.

    TAKE THIS COURSE

     

     

    COPYRIGHT © 2021 One Education