Booking options
£11.99
Course Images
Python for Data Science and Machine Learning Bootcamp
By Course Gate
(1)- 30 Day Money Back Guarantee
- Completion Certificate
- 24/7 Technical Support
Highlights
On-Demand course
10 hours 12 minutes
Intermediate level
Description
Python for Data Science and Machine Learning Bootcamp course is a comprehensive and practical online course designed to teach you how to use Python for data science and machine learning.
The course comprises several sections, each covering different topics in data science and machine learning. It includes all the essential concepts and tools required to analyse and manipulate data, build machine learning models, and make predictions or decisions.
It also covers dataset summary statistics and visualisation techniques using libraries like matplotlib and seaborn. You'll learn about model selection, ensemble approaches, and parameter adjustment. Additionally, you will discover how to export and load machine learning models using libraries such as pickle and joblib.
This course is suitable for anyone interested in learning Python for data science and machine learning. Whether you are an aspiring data scientist or a professional looking to enhance your data analysis skills, this course will help you achieve your goals.
Enrol now to gain instant access to the course materials and start your learning journey. Don't miss out on the opportunity to acquire one of the most in-demand skills in the market today and become a proficient data scientist and machine learning expert.
Learning Outcome of Python for Data Science and Machine Learning Bootcamp
Master fundamental concepts of machine learning and its applications.
Gain proficiency in Python programming for data analysis and manipulation.
Learn to load, read, and manipulate CSV data files using Python libraries.
Understand dataset summary statistics and visualization techniques.
Acquire skills in data preparation and preprocessing.
Explore feature selection techniques and their importance.
Evaluate machine learning algorithms using various techniques and metrics.
Spot check classification and regression algorithms for model selection.
Improve model performance using ensemble methods and parameter tuning.
Learn to export, save, load, and finalize machine learning models for real-time predictions.
Key Features of the Course
A CPD certificate that is recognised worldwide.
A great online learning experience.
Interesting and unique online materials and activities.
Expert guidance and support from the field leaders.
Access to the study resources anytime you want.
Friendly and helpful customer service and admin support by email, phone, and chat from Monday through Friday.
Get a year-long access to the course.
Who is this course for
This Python for Data Science and Machine Learning Bootcamp Course is suitable for -
Aspiring data scientists and machine learning enthusiasts
Professionals seeking to enhance their data analysis skills
Individuals interested in Python programming for data science
Anyone looking to break into the field of data science and machine learning
Students pursuing studies in computer science or related fields
Requirements
Basic understanding of programming concepts
Familiarity with Python programming language is beneficial but not mandatory
No prerequisites; suitable for individuals from any academic background.
Accessible course materials from any internet-enabled device.
CPD Certificate from Course Gate
At the successful completion of the course, you can obtain your CPD certificate from us. You can order the PDF certificate for £9 and the hard copy for £15. Also, you can order both PDF and hardcopy certificates for £22.
Career path
Data Scientist
Data Analyst
Machine Learning Engineer
Business Intelligence Analyst
Data Engineer
Research Scientist
Quantitative Analyst
Artificial Intelligence Specialist
Data Consultant
Statistician
course Content
90 sections•90 lessons
Course Overview & Table of Contents1 lesson
- Course Overview & Table of Contents
Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types1 lesson
- Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types
Introduction to Machine Learning - Part 2 - Classifications and Applications1 lesson
- Introduction to Machine Learning - Part 2 - Classifications and Applications
System and Environment preparation - Part 11 lesson
- System and Environment preparation - Part 1
System and Environment preparation - Part 21 lesson
- System and Environment preparation - Part 2
Learn Basics of python - Assignment1 lesson
- Learn Basics of python - Assignment 1
Learn Basics of python - Assignment1 lesson
- Learn Basics of python - Assignment 2
Learn Basics of python - Functions1 lesson
- Learn Basics of python - Functions
Learn Basics of python - Data Structures1 lesson
- Learn Basics of python - Data Structures
Learn Basics of NumPy - NumPy Array1 lesson
- Learn Basics of NumPy - NumPy Array
Learn Basics of NumPy - NumPy Data1 lesson
- Learn Basics of NumPy - NumPy Data
Learn Basics of NumPy - NumPy Arithmetic1 lesson
- Learn Basics of NumPy - NumPy Arithmetic
Learn Basics of Matplotlib1 lesson
- Learn Basics of Matplotlib
Learn Basics of Pandas - Part 11 lesson
- Learn Basics of Pandas - Part 1
Learn Basics of Pandas - Part 21 lesson
- Learn Basics of Pandas - Part 2
Understanding the CSV data file1 lesson
- Understanding the CSV data file
Load and Read CSV data file using Python Standard Library1 lesson
- Load and Read CSV data file using Python Standard Library
Load and Read CSV data file using NumPy1 lesson
- Load and Read CSV data file using NumPy
Load and Read CSV data file using Pandas1 lesson
- Load and Read CSV data file using Pandas
Dataset Summary - Peek, Dimensions and Data Types1 lesson
- Dataset Summary - Peek, Dimensions and Data Types
Dataset Summary - Class Distribution and Data Summary1 lesson
- Dataset Summary - Class Distribution and Data Summary
Dataset Summary - Explaining Correlation1 lesson
- Dataset Summary - Explaining Correlation
Dataset Summary - Explaining Skewness - Gaussian and Normal Curve1 lesson
- Dataset Summary - Explaining Skewness - Gaussian and Normal Curve
Dataset Visualization - Using Histograms1 lesson
- Dataset Visualization - Using Histograms
Dataset Visualization - Using Density Plots1 lesson
- Dataset Visualization - Using Density Plots
Dataset Visualization - Box and Whisker Plots1 lesson
- Dataset Visualization - Box and Whisker Plots
Multivariate Dataset Visualization - Correlation Plots1 lesson
- Multivariate Dataset Visualization - Correlation Plots
Multivariate Dataset Visualization - Scatter Plots1 lesson
- Multivariate Dataset Visualization - Scatter Plots
Data Preparation (Pre-Processing) - Introduction1 lesson
- Data Preparation (Pre-Processing) - Introduction
Data Preparation - Re-scaling Data - Part 11 lesson
- Data Preparation - Re-scaling Data - Part 1
Data Preparation - Re-scaling Data - Part 21 lesson
- Data Preparation - Re-scaling Data - Part 2
Data Preparation - Standardizing Data - Part 11 lesson
- Data Preparation - Standardizing Data - Part 1
Data Preparation - Standardizing Data - Part 21 lesson
- Data Preparation - Standardizing Data - Part 2
Data Preparation - Normalizing Data1 lesson
- Data Preparation - Normalizing Data
Data Preparation - Binarizing Data1 lesson
- Data Preparation - Binarizing Data
Feature Selection - Introduction1 lesson
- Feature Selection - Introduction
Feature Selection - Uni-variate Part 1 - Chi-Squared Test1 lesson
- Feature Selection - Uni-variate Part 1 - Chi-Squared Test
Feature Selection - Uni-variate Part 2 - Chi-Squared Test1 lesson
- Feature Selection - Uni-variate Part 2 - Chi-Squared Test
Feature Selection - Recursive Feature Elimination1 lesson
- Feature Selection - Recursive Feature Elimination
Feature Selection - Principal Component Analysis (PCA)1 lesson
- Feature Selection - Principal Component Analysis (PCA)
Feature Selection - Feature Importance1 lesson
- Feature Selection - Feature Importance
Refresher Session - The Mechanism of Re-sampling, Training and Testing1 lesson
- Refresher Session - The Mechanism of Re-sampling, Training and Testing
Algorithm Evaluation Techniques - Introduction1 lesson
- Algorithm Evaluation Techniques - Introduction
Algorithm Evaluation Techniques - Train and Test Set1 lesson
- Algorithm Evaluation Techniques - Train and Test Set
Algorithm Evaluation Techniques - K-Fold Cross Validation1 lesson
- Algorithm Evaluation Techniques - K-Fold Cross Validation
Algorithm Evaluation Techniques - Leave One Out Cross Validation1 lesson
- Algorithm Evaluation Techniques - Leave One Out Cross Validation
Algorithm Evaluation Techniques - Repeated Random Test-Train Splits1 lesson
- Algorithm Evaluation Techniques - Repeated Random Test-Train Splits
Algorithm Evaluation Metrics - Introduction1 lesson
- Algorithm Evaluation Metrics - Introduction
Algorithm Evaluation Metrics - Classification Accuracy1 lesson
- Algorithm Evaluation Metrics - Classification Accuracy
Algorithm Evaluation Metrics - Log Loss1 lesson
- Algorithm Evaluation Metrics - Log Loss
Algorithm Evaluation Metrics - Area Under ROC Curve1 lesson
- Algorithm Evaluation Metrics - Area Under ROC Curve
Algorithm Evaluation Metrics - Confusion Matrix1 lesson
- Algorithm Evaluation Metrics - Confusion Matrix
Algorithm Evaluation Metrics - Classification Report1 lesson
- Algorithm Evaluation Metrics - Classification Report
Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction1 lesson
- Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction
Algorithm Evaluation Metrics - Mean Absolute Error1 lesson
- Algorithm Evaluation Metrics - Mean Absolute Error
Algorithm Evaluation Metrics - Mean Square Error1 lesson
- Algorithm Evaluation Metrics - Mean Square Error
Algorithm Evaluation Metrics - R Squared1 lesson
- Algorithm Evaluation Metrics - R Squared
Classification Algorithm Spot Check - Logistic Regression1 lesson
- Classification Algorithm Spot Check - Logistic Regression
Classification Algorithm Spot Check - Linear Discriminant Analysis1 lesson
- Classification Algorithm Spot Check - Linear Discriminant Analysis
Classification Algorithm Spot Check - K-Nearest Neighbors1 lesson
- Classification Algorithm Spot Check - K-Nearest Neighbors
Classification Algorithm Spot Check - Naive Bayes1 lesson
- Classification Algorithm Spot Check - Naive Bayes
Classification Algorithm Spot Check - CART1 lesson
- Classification Algorithm Spot Check - CART
Classification Algorithm Spot Check - Support Vector Machines1 lesson
- Classification Algorithm Spot Check - Support Vector Machines
Regression Algorithm Spot Check - Linear Regression1 lesson
- Regression Algorithm Spot Check - Linear Regression
Regression Algorithm Spot Check - Ridge Regression1 lesson
- Regression Algorithm Spot Check - Ridge Regression
Regression Algorithm Spot Check - Lasso Linear Regression1 lesson
- Regression Algorithm Spot Check - Lasso Linear Regression
Regression Algorithm Spot Check - Elastic Net Regression1 lesson
- Regression Algorithm Spot Check - Elastic Net Regression
Regression Algorithm Spot Check - K-Nearest Neighbors1 lesson
- Regression Algorithm Spot Check - K-Nearest Neighbors
Regression Algorithm Spot Check - CART1 lesson
- Regression Algorithm Spot Check - CART
Regression Algorithm Spot Check - Support Vector Machines (SVM)1 lesson
- Regression Algorithm Spot Check - Support Vector Machines (SVM)
Compare Algorithms - Part 1 : Choosing the best Machine Learning Model1 lesson
- Compare Algorithms - Part 1 : Choosing the best Machine Learning Model
Compare Algorithms - Part 2 : Choosing the best Machine Learning Model1 lesson
- Compare Algorithms - Part 2 : Choosing the best Machine Learning Model
Pipelines : Data Preparation and Data Modelling1 lesson
- Pipelines : Data Preparation and Data Modelling
Pipelines : Feature Selection and Data Modelling1 lesson
- Pipelines : Feature Selection and Data Modelling
Performance Improvement: Ensembles - Voting1 lesson
- Performance Improvement: Ensembles - Voting
Performance Improvement: Ensembles - Bagging1 lesson
- Performance Improvement: Ensembles - Bagging
Performance Improvement: Ensembles - Boosting1 lesson
- Performance Improvement: Ensembles - Boosting
Performance Improvement: Parameter Tuning using Grid Search1 lesson
- Performance Improvement: Parameter Tuning using Grid Search
Performance Improvement: Parameter Tuning using Random Search1 lesson
- Performance Improvement: Parameter Tuning using Random Search
Export, Save and Load Machine Learning Models : Pickle1 lesson
- Export, Save and Load Machine Learning Models : Pickle
Export, Save and Load Machine Learning Models : Joblib1 lesson
- Export, Save and Load Machine Learning Models : Joblib
Finalizing a Model - Introduction and Steps1 lesson
- Finalizing a Model - Introduction and Steps
Finalizing a Classification Model - The Pima Indian Diabetes Dataset1 lesson
- Finalizing a Classification Model - The Pima Indian Diabetes Dataset
Quick Session: Imbalanced Data Set - Issue Overview and Steps1 lesson
- Quick Session: Imbalanced Data Set - Issue Overview and Steps
Iris Dataset : Finalizing Multi-Class Dataset1 lesson
- Iris Dataset : Finalizing Multi-Class Dataset
Finalizing a Regression Model - The Boston Housing Price Dataset1 lesson
- Finalizing a Regression Model - The Boston Housing Price Dataset
Real-time Predictions: Using the Pima Indian Diabetes Classification Model1 lesson
- Real-time Predictions: Using the Pima Indian Diabetes Classification Model
Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset1 lesson
- Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
Real-time Predictions: Using the Boston Housing Regression Model1 lesson
- Real-time Predictions: Using the Boston Housing Regression Model
Resources1 lesson
- Resources - Python for Machine Learning & Data Science
Reviews
There are no reviews for Python for Data Science and Machine Learning Bootcamp yet.Why not be the first one to review this course?
