Course Highlights
Do you want to advance your career and gain a strong foundation in Data Science & Machine Learning with Python? Our all-inclusive Data Science & Machine Learning with Python is the ideal remedy. This course, created by professionals in the field, offers a methodical approach to gaining the fundamental abilities and understanding required to succeed in Data Science & Machine Learning with Python . This course will assist you in understanding the fundamental ideas, instruments, and methods utilised in the industry, regardless of your experience level.
You will have valuable experience with exercises supporting your study through the Data Science & Machine Learning with Python . Our course module will ensure you understand the more complicated techniques and the essential ideas to help you grasp Data Science & Machine Learning with Python more deeply. Furthermore, networking with industry professionals and other students will improve both your academic and career opportunities.
After the course, you’ll be prepared to assume positions in Data Science & Machine Learning with Python on a professional level. As an asset to any organisation, your strong foundation of knowledge will be an asset. Additionally, your CV will provide you a competitive edge over other applicants by showcasing your accomplishments and skills. Start your journey to consistent income and career success by enrolling in our Data Science & Machine Learning with Python right now.
Note: Skill-up is a Janets-approved resale partner for Quality Licence Scheme Endorsed courses. Free QLS Certificate Included.
Learning outcome
- - Stay up-to-date with the latest advancements in Data Science & Machine Learning with Python.
- - Learn how to apply your theoretical knowledge in any professional environment.
- - Gain access to course materials created by industry professionals.
- - Study at your own pace, anytime and anywhere.
- - Get learner support from experts 24/7.
Course media
Certificate of Achievement
Quality Licence Scheme Endorsed Certificate
Upon completing the final assessment, you can apply for the Quality Licence Scheme Endorsed Certificate of Achievement. Endorsed certificates can be ordered and delivered to your home by post. An extra £10 postage charge will be required for students leaving overseas.
Free QLS Level 7 Certificate Included with this Data Science & Machine Learning with Python course.
An extra £10 postage charge will be required for students leaving overseas.
Skill Up Recognised Certificate
Upon successfully completing the Data Science & Machine Learning with Python course, you can request a Skill Up Recognised Certificate. This certificate holds significant value, and its validation will endure throughout your lifetime.
1. PDF Certificate + PDF Transcript: FREE With This Course
2. Hard Copy Certificate + Hard Copy Transcript: £19.99
3. Delivery Charge: £10.00 (Applicable for International Students)
Why should I take this course?
- - You will receive QLS Endorsed & CPD Accredited Certificate upon completing the course.
- - Affordable premium-quality e-learning content, you can learn at your own pace.
- - Internationally recognised Accredited Qualifications will boost up your resume.
- - You will learn the researched and proven approach successful people adopt to transform their careers.
- - You can incorporate various techniques successfully and understand your customers better.
Endorsement
This course has received endorsement by the Quality Licence Scheme for its exceptional quality, non-regulated training program. However, it is not regulated by Ofqual and does not offer accredited qualifications. Your training provider can advise you on prospective recognition opportunities, such as avenues to additional or higher education.
Method of Assessment
To assess your knowledge, you will take an automated multiple-choice exam. Passing and meeting the criteria for the Quality Licence Scheme-endorsed certificate requires a minimum score of 60%. Once you have achieved this score, you can apply for your certificate.
Furthermore, there are assignment questions at the end of the course that we highly recommend to answer. Completing these questions will help you understand your progress. You can answer them any time you want. The best thing is that our knowledgeable tutors will go over your work and provide insightful criticism.
Requirements
This Data Science & Machine Learning with Python is open to everyone, and no specific prerequisites are required. Anyone with an interest in the field can join!
With complete access to any internet-enabled device, you can learn anytime, anywhere—right from the comfort of your home.
Course Curriculum
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Course Overview & Table of Contents
00:09:00
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Introduction to Machine Learning – Part 1 – Concepts , Definitions and Types
00:05:00
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Introduction to Machine Learning – Part 2 – Classifications and Applications
00:06:00
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System and Environment preparation – Part 1
00:08:00
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System and Environment preparation – Part 2
00:06:00
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Learn Basics of python – Assignment 2
00:09:00
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Learn Basics of python – Functions
00:04:00
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Learn Basics of python – Data Structures
00:12:00
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Learn Basics of NumPy – NumPy Array
00:06:00
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Learn Basics of NumPy – NumPy Data
00:08:00
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Learn Basics of NumPy – NumPy Arithmetic
00:04:00
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Learn Basics of Matplotlib
00:07:00
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Learn Basics of Pandas – Part 1
00:06:00
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Learn Basics of Pandas – Part 2
00:07:00
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Understanding the CSV data file
00:09:00
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Load and Read CSV data file using Python Standard Library
00:09:00
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Load and Read CSV data file using NumPy
00:04:00
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Load and Read CSV data file using Pandas
00:05:00
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Dataset Summary – Peek, Dimensions and Data Types
00:09:00
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Dataset Summary – Class Distribution and Data Summary
00:09:00
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Dataset Summary – Explaining Correlation
00:11:00
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Dataset Summary – Explaining Skewness – Gaussian and Normal Curve
00:07:00
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Dataset Visualization – Using Histograms
00:07:00
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Dataset Visualization – Using Density Plots
00:06:00
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Dataset Visualization – Box and Whisker Plots
00:05:00
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Multivariate Dataset Visualization – Correlation Plots
00:08:00
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Multivariate Dataset Visualization – Scatter Plots
00:05:00
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Data Preparation (Pre-Processing) – Introduction
00:09:00
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Data Preparation – Re-scaling Data – Part 1
00:09:00
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Data Preparation – Re-scaling Data – Part 2
00:09:00
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Data Preparation – Standardizing Data – Part 1
00:07:00
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Data Preparation – Standardizing Data – Part 2
00:04:00
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Data Preparation – Normalizing Data
00:08:00
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Data Preparation – Binarizing Data
00:06:00
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Feature Selection – Introduction
00:07:00
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Feature Selection – Uni-variate Part 1 – Chi-Squared Test
00:09:00
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Feature Selection – Uni-variate Part 2 – Chi-Squared Test
00:10:00
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Feature Selection – Recursive Feature Elimination
00:11:00
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Feature Selection – Principal Component Analysis (PCA)
00:09:00
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Feature Selection – Feature Importance
00:07:00
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Refresher Session – The Mechanism of Re-sampling, Training and Testing
00:12:00
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Algorithm Evaluation Techniques – Introduction
00:07:00
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Algorithm Evaluation Techniques – Train and Test Set
00:11:00
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Algorithm Evaluation Techniques – K-Fold Cross Validation
00:09:00
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Algorithm Evaluation Techniques – Leave One Out Cross Validation
00:05:00
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Algorithm Evaluation Techniques – Repeated Random Test-Train Splits
00:07:00
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Algorithm Evaluation Metrics – Introduction
00:09:00
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Algorithm Evaluation Metrics – Classification Accuracy
00:08:00
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Algorithm Evaluation Metrics – Log Loss
00:03:00
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Algorithm Evaluation Metrics – Area Under ROC Curve
00:06:00
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Algorithm Evaluation Metrics – Classification Report
00:04:00
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Algorithm Evaluation Metrics – Mean Absolute Error – Dataset Introduction
00:06:00
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Algorithm Evaluation Metrics – Mean Absolute Error
00:07:00
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Algorithm Evaluation Metrics – Mean Square Error
00:03:00
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Algorithm Evaluation Metrics – R Squared
00:04:00
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Classification Algorithm Spot Check – Logistic Regression
00:12:00
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Classification Algorithm Spot Check – Linear Discriminant Analysis
00:04:00
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Classification Algorithm Spot Check – K-Nearest Neighbors
00:05:00
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Classification Algorithm Spot Check – Naive Bayes
00:04:00
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Classification Algorithm Spot Check – CART
00:04:00
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Classification Algorithm Spot Check – Support Vector Machines
00:05:00
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Regression Algorithm Spot Check – Linear Regression
00:08:00
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Regression Algorithm Spot Check – Ridge Regression
00:03:00
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Regression Algorithm Spot Check – Lasso Linear Regression
00:03:00
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Regression Algorithm Spot Check – Elastic Net Regression
00:02:00
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Regression Algorithm Spot Check – K-Nearest Neighbors
00:06:00
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Regression Algorithm Spot Check – CART
00:04:00
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Regression Algorithm Spot Check – Support Vector Machines (SVM)
00:04:00
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Compare Algorithms – Part 1 : Choosing the best Machine Learning Model
00:09:00
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Compare Algorithms – Part 2 : Choosing the best Machine Learning Model
00:05:00
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Pipelines : Data Preparation and Data Modelling
00:11:00
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Pipelines : Feature Selection and Data Modelling
00:10:00
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Performance Improvement: Ensembles – Voting
00:07:00
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Performance Improvement: Ensembles – Bagging
00:08:00
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Performance Improvement: Ensembles – Boosting
00:05:00
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Performance Improvement: Parameter Tuning using Grid Search
00:08:00
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Performance Improvement: Parameter Tuning using Random Search
00:06:00
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Export, Save and Load Machine Learning Models : Pickle
00:10:00
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Export, Save and Load Machine Learning Models : Joblib
00:06:00
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Finalizing a Model – Introduction and Steps
00:07:00
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Finalizing a Classification Model – The Pima Indian Diabetes Dataset
00:07:00
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Quick Session: Imbalanced Data Set – Issue Overview and Steps
00:09:00
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Iris Dataset : Finalizing Multi-Class Dataset
00:09:00
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Finalizing a Regression Model – The Boston Housing Price Dataset
00:08:00
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Real-time Predictions: Using the Pima Indian Diabetes Classification Model
00:07:00
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Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
00:03:00
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Real-time Predictions: Using the Boston Housing Regression Model
00:08:00
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Resources – Data Science & Machine Learning with Python
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Claim Your Certificate
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Duration:Self-paced Learning
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Access:1 Year
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Units:91
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