Name: Data Science & Machine Learning With Python
Price: 25 GBP
Availability: InStock

Learning Outcomes
Learn Python for data analysis using NumPy and Pandas
Obtain a clear understanding of datasets visualisation
Become more proficient in principal component analysis
Gain knowledge about algorithm evaluation techniques
Improve your knowledge of performance improvement
Equip yourself with the skills for data preparations and data modelling
Description
A survey comes with result that, over 78% of data scientists, data analysts and software engineers use Python over any other programming language for their work. So, if you want to launch your career in any of these fields, how can you shine without having expert knowledge of Python? We formulated this Data Science & Machine Learning With Python course to give you a thorough understanding of this matter.
In this comprehensive course, you will receive detailed information about Python programming language. The course will deliver elaborate lessons on NumPy and Pandas. Furthermore, while progressing with the study you will get to learn how to do data analysis, and visualisation using Python. Along with that, the course will give you a clear picture of algorithm evaluation techniques, principal component analysis and much more.
Upon the successful completion of this course, you will get a QLS- Endorsed certificate of achievement, which can help you grab the attention of employers. So, what are you waiting for? Join us now to begin your learning journey.
Certificate of Achievement
Endorsed Certificate of Achievement from the Quality Licence Scheme
Upon successful completion of the final assessment, you will be eligible to apply for the Quality Licence Scheme Endorsed Certificate of achievement. This certificate will be delivered to your doorstep through the post for £119. An extra £10 postage charge will be required for students leaving overseas. 
CPD Accredited Certificate
After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post.
Method of Assessment
At the end of the course, there will be a final assessment. A set of questions will be provided, and you can complete these questions according to your convenient time. After you submit the assignment, our expert team will evaluate them and provide constructive feedback.
Career path
We designed this course not only for improving your knowledge of Python but also to prepare you for job opportunities. Some of them are given in the down below –
Web Developer
Software Engineer
Data Scientist
Machine Learning Engineer
Data Analyst
Course Contents
Course Overview & Table of Contents
Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types
Introduction to Machine Learning - Part 2 - Classifications and Applications
System and Environment preparation - Part 1
System and Environment preparation - Part 2
Learn Basics of python - Assignment
Learn Basics of python - Assignment
Learn Basics of python - Functions
Learn Basics of python - Data Structures
Learn Basics of NumPy - NumPy Array
Learn Basics of NumPy - NumPy Data
Learn Basics of NumPy - NumPy Arithmetic
Learn Basics of Matplotlib
Learn Basics of Pandas - Part 1
Learn Basics of Pandas - Part 2
Understanding the CSV data file
Load and Read CSV data file using Python Standard Library
Load and Read CSV data file using NumPy
Load and Read CSV data file using Pandas
Dataset Summary - Peek, Dimensions and Data Types
Dataset Summary - Class Distribution and Data Summary
Dataset Summary - Explaining Correlation
Dataset Summary - Explaining Skewness - Gaussian and Normal Curve
Dataset Visualization - Using Histograms
Dataset Visualization - Using Density Plots
Dataset Visualization - Box and Whisker Plots
Multivariate Dataset Visualization - Correlation Plots
Multivariate Dataset Visualization - Scatter Plots
Data Preparation (Pre-Processing) - Introduction
Data Preparation - Re-scaling Data - Part 1
Data Preparation - Re-scaling Data - Part 2
Data Preparation - Standardizing Data - Part 1
Data Preparation - Standardizing Data - Part 2
Data Preparation - Normalizing Data
Data Preparation - Binarizing Data
Feature Selection - Introduction
Feature Selection - Uni-variate Part 1 - Chi-Squared Test
Feature Selection - Uni-variate Part 2 - Chi-Squared Test
Feature Selection - Recursive Feature Elimination
Feature Selection - Principal Component Analysis (PCA)
Feature Selection - Feature Importance
Refresher Session - The Mechanism of Re-sampling, Training and Testing
Algorithm Evaluation Techniques - Introduction
Algorithm Evaluation Techniques - Train and Test Set
Algorithm Evaluation Techniques - K-Fold Cross Validation
Algorithm Evaluation Techniques - Leave One Out Cross Validation
Algorithm Evaluation Techniques - Repeated Random Test-Train Splits
Algorithm Evaluation Metrics - Introduction
Algorithm Evaluation Metrics - Classification Accuracy
Algorithm Evaluation Metrics - Log Loss
Algorithm Evaluation Metrics - Area Under ROC Curve
Algorithm Evaluation Metrics - Confusion Matrix
Algorithm Evaluation Metrics - Classification Report
Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction
Algorithm Evaluation Metrics - Mean Absolute Error
Algorithm Evaluation Metrics - Mean Square Error
Algorithm Evaluation Metrics - R Squared
Classification Algorithm Spot Check - Logistic Regression
Classification Algorithm Spot Check - Linear Discriminant Analysis
Classification Algorithm Spot Check - K-Nearest Neighbors
Classification Algorithm Spot Check - Naive Bayes
Classification Algorithm Spot Check - CART
Classification Algorithm Spot Check - Support Vector Machines
Regression Algorithm Spot Check - Linear Regression
Regression Algorithm Spot Check - Ridge Regression
Regression Algorithm Spot Check - Lasso Linear Regression
Regression Algorithm Spot Check - Elastic Net Regression
Regression Algorithm Spot Check - K-Nearest Neighbors
Regression Algorithm Spot Check - CART
Regression Algorithm Spot Check - Support Vector Machines (SVM)
Compare Algorithms - Part 1 : Choosing the best Machine Learning Model
Compare Algorithms - Part 2 : Choosing the best Machine Learning Model
Pipelines : Data Preparation and Data Modelling
Pipelines : Feature Selection and Data Modelling
Performance Improvement: Ensembles - Voting
Performance Improvement: Ensembles - Bagging
Performance Improvement: Ensembles - Boosting
Performance Improvement: Parameter Tuning using Grid Search
Performance Improvement: Parameter Tuning using Random Search
Export, Save and Load Machine Learning Models : Pickle
Export, Save and Load Machine Learning Models : Joblib
Finalizing a Model - Introduction and Steps
Finalizing a Classification Model - The Pima Indian Diabetes Dataset
Quick Session: Imbalanced Data Set - Issue Overview and Steps
Iris Dataset : Finalizing Multi-Class Dataset
Finalizing a Regression Model - The Boston Housing Price Dataset
Real-time Predictions: Using the Pima Indian Diabetes Classification Model
Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
Real-time Predictions: Using the Boston Housing Regression Model

Janets

Coordinate Measuring Machine (CMM)

Machine Learning (ML)

Data Science

Python

Discover the power of data science and machine learning with Python! Learn essential techniques, algorithms, and tools to analyze data, build predictive models, and unlock insights. Dive into hands-on projects, from data manipulation to advanced machine learning applications. Elevate your skills and unleash the potential of Python for data-driven decision-making.

Data Science & Machine Learning With Python

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Data Science & Machine Learning With Python

By Janets