Data Science & Machine Learning with Python

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6 STUDENTS
Data Science & Machine Learning with Python

Educational accomplishments and income are closely correlated. Education and skill with endorsed certificates from credible and renowned authorities typically lead to better jobs with attractive salaries. Educated and skilled workers also have lower rates of unemployment. Therefore, skills and endorsed certificates to showcase are essential for people early in their careers.

Imperial Academy offers this premium Data Science & Machine Learning with Python course to ensure security in your career. In addition, this comprehensive Data Science & Machine Learning with Python course will assist you in building relevant skills that will help you find a job in the related sectors. Also, the Certificate you’ll get after completing the Data Science & Machine Learning with Python will put your head and shoulder above others in front of potential employers.

Become the person who would attract the results you seek. What you plant now, you will harvest later. So, grab this opportunity and start learning Data Science & Machine Learning with Python!

Learn at your own pace from the comfort of your home, as the rich learning materials of this premium course is accessible from any place at any time. The advanced course curriculums are divided into tiny bite-sized audio-visual modules by industry specialists with years of experience behind them.

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 – Data Science & Machine Learning with Python 00:00:00

Certificate of Achievement

Learners will get an certificate of achievement directly at their doorstep after successfully completing the course!

It should also be noted that international students must pay £10 for shipping cost.

CPD Accredited Certification

Upon successfully completing the course, you will be qualified for CPD Accredited Certificate. Certification is available –

Course Info

4.7
6 Enrolled
  • Development
  • IT & Software
TAKE THIS COURSE
  • £25
  • 1 year
  • Number of Units90
  • Number of Quizzes0
  • 10 hours, 19 minutes

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