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Python for Data Science and Machine Learning Bootcamp

Python for Data Science and Machine Learning Bootcamp

By Course Gate

5.0(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

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

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Course Gate
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London
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