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Data Science & Machine Learning with Python

CPD Accredited Course

  • Calendar 6 Students
  • Calendar 1 Year
  • calendar Intermediate
  • clock 10 hours, 24 minutes

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Course Overview

Gear up to get well equipped in Data Science & Machine Learning with Python now, make up your mind to be the best in the business! All you need is proper training, firm support and push to shine in your vocation, and Compliance Central is determined to provide you with it all! Explore what you got in this exclusive Data Science & Machine Learning with Python, start learning and excel in it! This amazing Data Science & Machine Learning with Python course has been designed and developed by the industry specialists who had been to the business for years, went through ups-and-downs, climbed up the success ladder with sheer excellence! You’ll get to-the-point knowledge both practical and theoretical, and gain valuable insights on the business which will help you understand the drill better than ever! Join today, be skilled, learn with positive energy and enthusiasm, create an excellent career using your full potential! Brace yourself, enrol now for an amazing venture!

This Data Science & Machine Learning with Python Course Package Includes

  • Comprehensive lessons and training provided by experts on Data Science & Machine Learning with Python
  • Interactive online learning experience provided by qualified professionals in your convenience
  • 24/7 Access to the course materials and learner assistance
  • Easy accessibility from any smart device (Laptop, Tablet, Smartphone etc.)
  • A happy and handy learning experience for the professionals and students
  • 100% learning satisfaction, guaranteed by Compliance Central

Learning Outcome

Upon successful completion of this highly appreciated Data Science & Machine Learning with Python course, you’ll be a skilled professional, besides—
  • You can provide services related to Data Science & Machine Learning with Python with complete knowledge and confidence
  • You’ll be competent and proficient enough to start a Data Science & Machine Learning with Python related business on your own
  • You can train up others and grow an efficient peer community on your locality and serve people
  • It will enhance your portfolio, you can use the certificate as proof of your efficiency to the employer
  • It will boost up your productivity, you can use the skill and credentials, and become more competent in your vocation with increased earning!

Certification

You can instantly download your certificate for £4.79 right after finishing the Data Science & Machine Learning with Python course. The hard copy of the certification will also be sent right at your doorstep via post for £10.79. All of our courses are continually reviewed to ensure their quality, and that provide appropriate current training for your chosen subject. As such, although certificates do not expire, it is recommended that they are reviewed or renewed on an annual basis.

Who Is This Course For

Compliance Central aims to prepare efficient human resources for the industry and make it more productive than ever. This helpful course is suitable for any person who is interested in Data Science & Machine Learning with Python. There are no pre-requirements to take it. You can attend the course if you are a student, an enthusiast or a
  • Employee
  • Employer
  • Manager
  • Supervisor
  • Entrepreneur
  • Business Professional
  • Company Leader
  • HR Professional

Course Currilcum

    • Course Overview & Table of Contents 00:09:00
    • Introduction to Machine Learning – Part 1 – Concepts , Definitions and Types 00:05:00
    • Introduction to Machine Learning – Part 2 – Classifications and Applications 00:06:00
    • System and Environment preparation – Part 1 00:08:00
    • System and Environment preparation – Part 2 00:06:00
    • Learn Basics of python – Assignment 1 00:10:00
    • Learn Basics of python – Assignment 2 00:09:00
    • Learn Basics of python – Functions 00:04:00
    • Learn Basics of python – Data Structures 00:12:00
    • Learn Basics of NumPy – NumPy Array 00:06:00
    • Learn Basics of NumPy – NumPy Data 00:08:00
    • Learn Basics of NumPy – NumPy Arithmetic 00:04:00
    • Learn Basics of Matplotlib 00:07:00
    • Learn Basics of Pandas – Part 1 00:06:00
    • Learn Basics of Pandas – Part 2 00:07:00
    • Understanding the CSV data file 00:09:00
    • Load and Read CSV data file using Python Standard Library 00:09:00
    • Load and Read CSV data file using NumPy 00:04:00
    • Load and Read CSV data file using Pandas 00:05:00
    • Dataset Summary – Peek, Dimensions and Data Types 00:09:00
    • Dataset Summary – Class Distribution and Data Summary 00:09:00
    • Dataset Summary – Explaining Correlation 00:11:00
    • Dataset Summary – Explaining Skewness – Gaussian and Normal Curve 00:07:00
    • Dataset Visualization – Using Histograms 00:07:00
    • Dataset Visualization – Using Density Plots 00:06:00
    • Dataset Visualization – Box and Whisker Plots 00:05:00
    • Multivariate Dataset Visualization – Correlation Plots 00:08:00
    • Multivariate Dataset Visualization – Scatter Plots 00:05:00
    • Data Preparation (Pre-Processing) – Introduction 00:09:00
    • Data Preparation – Re-scaling Data – Part 1 00:09:00
    • Data Preparation – Re-scaling Data – Part 2 00:09:00
    • Data Preparation – Standardizing Data – Part 1 00:07:00
    • Data Preparation – Standardizing Data – Part 2 00:04:00
    • Data Preparation – Normalizing Data 00:08:00
    • Data Preparation – Binarizing Data 00:06:00
    • Feature Selection – Introduction 00:07:00
    • Feature Selection – Uni-variate Part 1 – Chi-Squared Test 00:09:00
    • Feature Selection – Uni-variate Part 2 – Chi-Squared Test 00:10:00
    • Feature Selection – Recursive Feature Elimination 00:11:00
    • Feature Selection – Principal Component Analysis (PCA) 00:09:00
    • Feature Selection – Feature Importance 00:07:00
    • Refresher Session – The Mechanism of Re-sampling, Training and Testing 00:12:00
    • Algorithm Evaluation Techniques – Introduction 00:07:00
    • Algorithm Evaluation Techniques – Train and Test Set 00:11:00
    • Algorithm Evaluation Techniques – K-Fold Cross Validation 00:09:00
    • Algorithm Evaluation Techniques – Leave One Out Cross Validation 00:05:00
    • Algorithm Evaluation Techniques – Repeated Random Test-Train Splits 00:07:00
    • Algorithm Evaluation Metrics – Introduction 00:09:00
    • Algorithm Evaluation Metrics – Classification Accuracy 00:08:00
    • Algorithm Evaluation Metrics – Log Loss 00:03:00
    • Algorithm Evaluation Metrics – Area Under ROC Curve 00:06:00
    • Algorithm Evaluation Metrics – Confusion Matrix 00:10:00
    • Algorithm Evaluation Metrics – Classification Report 00:04:00
    • Algorithm Evaluation Metrics – Mean Absolute Error – Dataset Introduction 00:06:00
    • Algorithm Evaluation Metrics – Mean Absolute Error 00:07:00
    • Algorithm Evaluation Metrics – Mean Square Error 00:03:00
    • Algorithm Evaluation Metrics – R Squared 00:04:00
    • Classification Algorithm Spot Check – Logistic Regression 00:12:00
    • Classification Algorithm Spot Check – Linear Discriminant Analysis 00:04:00
    • Classification Algorithm Spot Check – K-Nearest Neighbors 00:05:00
    • Classification Algorithm Spot Check – Naive Bayes 00:04:00
    • Classification Algorithm Spot Check – CART 00:04:00
    • Classification Algorithm Spot Check – Support Vector Machines 00:05:00
    • Regression Algorithm Spot Check – Linear Regression 00:08:00
    • Regression Algorithm Spot Check – Ridge Regression 00:03:00
    • Regression Algorithm Spot Check – Lasso Linear Regression 00:03:00
    • Regression Algorithm Spot Check – Elastic Net Regression 00:02:00
    • Regression Algorithm Spot Check – K-Nearest Neighbors 00:06:00
    • Regression Algorithm Spot Check – CART 00:04:00
    • Regression Algorithm Spot Check – Support Vector Machines (SVM) 00:04:00
    • Compare Algorithms – Part 1 : Choosing the best Machine Learning Model 00:09:00
    • Compare Algorithms – Part 2 : Choosing the best Machine Learning Model 00:05:00
    • Pipelines : Data Preparation and Data Modelling 00:11:00
    • Pipelines : Feature Selection and Data Modelling 00:10:00
    • Performance Improvement: Ensembles – Voting 00:07:00
    • Performance Improvement: Ensembles – Bagging 00:08:00
    • Performance Improvement: Ensembles – Boosting 00:05:00
    • Performance Improvement: Parameter Tuning using Grid Search 00:08:00
    • Performance Improvement: Parameter Tuning using Random Search 00:06:00
    • Export, Save and Load Machine Learning Models : Pickle 00:10:00
    • Export, Save and Load Machine Learning Models : Joblib 00:06:00
    • Finalizing a Model – Introduction and Steps 00:07:00
    • Finalizing a Classification Model – The Pima Indian Diabetes Dataset 00:07:00
    • Quick Session: Imbalanced Data Set – Issue Overview and Steps 00:09:00
    • Iris Dataset : Finalizing Multi-Class Dataset 00:09:00
    • Finalizing a Regression Model – The Boston Housing Price Dataset 00:08:00
    • Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00
    • Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00
    • Real-time Predictions: Using the Boston Housing Regression Model 00:08:00

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Student Reviews

Ben lim

Gaining improve knowledge in the construction project management and the course is easy to understand.

Mr Brian Joseph Keenan

Very good and informative and quick with marking my assignments and issuing my certificate.

Sarah D

Being a support worker I needed add a child care cert in my portfolio. I have done the course and that was really a good course.

Sam Ryder

The first aid course was very informative with well organised curriculum. I already have some bit and pieces knowledge of first aid, this course helped me a lot.

Ben lim

Gaining improve knowledge in the construction project management and the course is easy to understand.

Thelma Gittens

Highly recommended. The module is easy to understand and definitely the best value for money. Many thanks

BF Carey

First course with Compliance Central. It was a good experience.

Course Currilcum

    • Course Overview & Table of Contents 00:09:00
    • Introduction to Machine Learning – Part 1 – Concepts , Definitions and Types 00:05:00
    • Introduction to Machine Learning – Part 2 – Classifications and Applications 00:06:00
    • System and Environment preparation – Part 1 00:08:00
    • System and Environment preparation – Part 2 00:06:00
    • Learn Basics of python – Assignment 1 00:10:00
    • Learn Basics of python – Assignment 2 00:09:00
    • Learn Basics of python – Functions 00:04:00
    • Learn Basics of python – Data Structures 00:12:00
    • Learn Basics of NumPy – NumPy Array 00:06:00
    • Learn Basics of NumPy – NumPy Data 00:08:00
    • Learn Basics of NumPy – NumPy Arithmetic 00:04:00
    • Learn Basics of Matplotlib 00:07:00
    • Learn Basics of Pandas – Part 1 00:06:00
    • Learn Basics of Pandas – Part 2 00:07:00
    • Understanding the CSV data file 00:09:00
    • Load and Read CSV data file using Python Standard Library 00:09:00
    • Load and Read CSV data file using NumPy 00:04:00
    • Load and Read CSV data file using Pandas 00:05:00
    • Dataset Summary – Peek, Dimensions and Data Types 00:09:00
    • Dataset Summary – Class Distribution and Data Summary 00:09:00
    • Dataset Summary – Explaining Correlation 00:11:00
    • Dataset Summary – Explaining Skewness – Gaussian and Normal Curve 00:07:00
    • Dataset Visualization – Using Histograms 00:07:00
    • Dataset Visualization – Using Density Plots 00:06:00
    • Dataset Visualization – Box and Whisker Plots 00:05:00
    • Multivariate Dataset Visualization – Correlation Plots 00:08:00
    • Multivariate Dataset Visualization – Scatter Plots 00:05:00
    • Data Preparation (Pre-Processing) – Introduction 00:09:00
    • Data Preparation – Re-scaling Data – Part 1 00:09:00
    • Data Preparation – Re-scaling Data – Part 2 00:09:00
    • Data Preparation – Standardizing Data – Part 1 00:07:00
    • Data Preparation – Standardizing Data – Part 2 00:04:00
    • Data Preparation – Normalizing Data 00:08:00
    • Data Preparation – Binarizing Data 00:06:00
    • Feature Selection – Introduction 00:07:00
    • Feature Selection – Uni-variate Part 1 – Chi-Squared Test 00:09:00
    • Feature Selection – Uni-variate Part 2 – Chi-Squared Test 00:10:00
    • Feature Selection – Recursive Feature Elimination 00:11:00
    • Feature Selection – Principal Component Analysis (PCA) 00:09:00
    • Feature Selection – Feature Importance 00:07:00
    • Refresher Session – The Mechanism of Re-sampling, Training and Testing 00:12:00
    • Algorithm Evaluation Techniques – Introduction 00:07:00
    • Algorithm Evaluation Techniques – Train and Test Set 00:11:00
    • Algorithm Evaluation Techniques – K-Fold Cross Validation 00:09:00
    • Algorithm Evaluation Techniques – Leave One Out Cross Validation 00:05:00
    • Algorithm Evaluation Techniques – Repeated Random Test-Train Splits 00:07:00
    • Algorithm Evaluation Metrics – Introduction 00:09:00
    • Algorithm Evaluation Metrics – Classification Accuracy 00:08:00
    • Algorithm Evaluation Metrics – Log Loss 00:03:00
    • Algorithm Evaluation Metrics – Area Under ROC Curve 00:06:00
    • Algorithm Evaluation Metrics – Confusion Matrix 00:10:00
    • Algorithm Evaluation Metrics – Classification Report 00:04:00
    • Algorithm Evaluation Metrics – Mean Absolute Error – Dataset Introduction 00:06:00
    • Algorithm Evaluation Metrics – Mean Absolute Error 00:07:00
    • Algorithm Evaluation Metrics – Mean Square Error 00:03:00
    • Algorithm Evaluation Metrics – R Squared 00:04:00
    • Classification Algorithm Spot Check – Logistic Regression 00:12:00
    • Classification Algorithm Spot Check – Linear Discriminant Analysis 00:04:00
    • Classification Algorithm Spot Check – K-Nearest Neighbors 00:05:00
    • Classification Algorithm Spot Check – Naive Bayes 00:04:00
    • Classification Algorithm Spot Check – CART 00:04:00
    • Classification Algorithm Spot Check – Support Vector Machines 00:05:00
    • Regression Algorithm Spot Check – Linear Regression 00:08:00
    • Regression Algorithm Spot Check – Ridge Regression 00:03:00
    • Regression Algorithm Spot Check – Lasso Linear Regression 00:03:00
    • Regression Algorithm Spot Check – Elastic Net Regression 00:02:00
    • Regression Algorithm Spot Check – K-Nearest Neighbors 00:06:00
    • Regression Algorithm Spot Check – CART 00:04:00
    • Regression Algorithm Spot Check – Support Vector Machines (SVM) 00:04:00
    • Compare Algorithms – Part 1 : Choosing the best Machine Learning Model 00:09:00
    • Compare Algorithms – Part 2 : Choosing the best Machine Learning Model 00:05:00
    • Pipelines : Data Preparation and Data Modelling 00:11:00
    • Pipelines : Feature Selection and Data Modelling 00:10:00
    • Performance Improvement: Ensembles – Voting 00:07:00
    • Performance Improvement: Ensembles – Bagging 00:08:00
    • Performance Improvement: Ensembles – Boosting 00:05:00
    • Performance Improvement: Parameter Tuning using Grid Search 00:08:00
    • Performance Improvement: Parameter Tuning using Random Search 00:06:00
    • Export, Save and Load Machine Learning Models : Pickle 00:10:00
    • Export, Save and Load Machine Learning Models : Joblib 00:06:00
    • Finalizing a Model – Introduction and Steps 00:07:00
    • Finalizing a Classification Model – The Pima Indian Diabetes Dataset 00:07:00
    • Quick Session: Imbalanced Data Set – Issue Overview and Steps 00:09:00
    • Iris Dataset : Finalizing Multi-Class Dataset 00:09:00
    • Finalizing a Regression Model – The Boston Housing Price Dataset 00:08:00
    • Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00
    • Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00
    • Real-time Predictions: Using the Boston Housing Regression Model 00:08:00