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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

90 sections•90 lessons
Course Overview & Table of Contents1 lessons
  1. 1Course Overview & Table of Contents
Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types1 lessons
  1. 1Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types
Introduction to Machine Learning - Part 2 - Classifications and Applications1 lessons
  1. 1Introduction to Machine Learning - Part 2 - Classifications and Applications
System and Environment preparation - Part 11 lessons
  1. 1System and Environment preparation - Part 1
System and Environment preparation - Part 21 lessons
  1. 1System and Environment preparation - Part 2
Learn Basics of python - Assignment1 lessons
  1. 1Learn Basics of python - Assignment 1
Learn Basics of python - Assignment1 lessons
  1. 1Learn Basics of python - Assignment 2
Learn Basics of python - Functions1 lessons
  1. 1Learn Basics of python - Functions
Learn Basics of python - Data Structures1 lessons
  1. 1Learn Basics of python - Data Structures
Learn Basics of NumPy - NumPy Array1 lessons
  1. 1Learn Basics of NumPy - NumPy Array
Learn Basics of NumPy - NumPy Data1 lessons
  1. 1Learn Basics of NumPy - NumPy Data
Learn Basics of NumPy - NumPy Arithmetic1 lessons
  1. 1Learn Basics of NumPy - NumPy Arithmetic
Learn Basics of Matplotlib1 lessons
  1. 1Learn Basics of Matplotlib
Learn Basics of Pandas - Part 11 lessons
  1. 1Learn Basics of Pandas - Part 1
Learn Basics of Pandas - Part 21 lessons
  1. 1Learn Basics of Pandas - Part 2
Understanding the CSV data file1 lessons
  1. 1Understanding the CSV data file
Load and Read CSV data file using Python Standard Library1 lessons
  1. 1Load and Read CSV data file using Python Standard Library
Load and Read CSV data file using NumPy1 lessons
  1. 1Load and Read CSV data file using NumPy
Load and Read CSV data file using Pandas1 lessons
  1. 1Load and Read CSV data file using Pandas
Dataset Summary - Peek, Dimensions and Data Types1 lessons
  1. 1Dataset Summary - Peek, Dimensions and Data Types
Dataset Summary - Class Distribution and Data Summary1 lessons
  1. 1Dataset Summary - Class Distribution and Data Summary
Dataset Summary - Explaining Correlation1 lessons
  1. 1Dataset Summary - Explaining Correlation
Dataset Summary - Explaining Skewness - Gaussian and Normal Curve1 lessons
  1. 1Dataset Summary - Explaining Skewness - Gaussian and Normal Curve
Dataset Visualization - Using Histograms1 lessons
  1. 1Dataset Visualization - Using Histograms
Dataset Visualization - Using Density Plots1 lessons
  1. 1Dataset Visualization - Using Density Plots
Dataset Visualization - Box and Whisker Plots1 lessons
  1. 1Dataset Visualization - Box and Whisker Plots
Multivariate Dataset Visualization - Correlation Plots1 lessons
  1. 1Multivariate Dataset Visualization - Correlation Plots
Multivariate Dataset Visualization - Scatter Plots1 lessons
  1. 1Multivariate Dataset Visualization - Scatter Plots
Data Preparation (Pre-Processing) - Introduction1 lessons
  1. 1Data Preparation (Pre-Processing) - Introduction
Data Preparation - Re-scaling Data - Part 11 lessons
  1. 1Data Preparation - Re-scaling Data - Part 1
Data Preparation - Re-scaling Data - Part 21 lessons
  1. 1Data Preparation - Re-scaling Data - Part 2
Data Preparation - Standardizing Data - Part 11 lessons
  1. 1Data Preparation - Standardizing Data - Part 1
Data Preparation - Standardizing Data - Part 21 lessons
  1. 1Data Preparation - Standardizing Data - Part 2
Data Preparation - Normalizing Data1 lessons
  1. 1Data Preparation - Normalizing Data
Data Preparation - Binarizing Data1 lessons
  1. 1Data Preparation - Binarizing Data
Feature Selection - Introduction1 lessons
  1. 1Feature Selection - Introduction
Feature Selection - Uni-variate Part 1 - Chi-Squared Test1 lessons
  1. 1Feature Selection - Uni-variate Part 1 - Chi-Squared Test
Feature Selection - Uni-variate Part 2 - Chi-Squared Test1 lessons
  1. 1Feature Selection - Uni-variate Part 2 - Chi-Squared Test
Feature Selection - Recursive Feature Elimination1 lessons
  1. 1Feature Selection - Recursive Feature Elimination
Feature Selection - Principal Component Analysis (PCA)1 lessons
  1. 1Feature Selection - Principal Component Analysis (PCA)
Feature Selection - Feature Importance1 lessons
  1. 1Feature Selection - Feature Importance
Refresher Session - The Mechanism of Re-sampling, Training and Testing1 lessons
  1. 1Refresher Session - The Mechanism of Re-sampling, Training and Testing
Algorithm Evaluation Techniques - Introduction1 lessons
  1. 1Algorithm Evaluation Techniques - Introduction
Algorithm Evaluation Techniques - Train and Test Set1 lessons
  1. 1Algorithm Evaluation Techniques - Train and Test Set
Algorithm Evaluation Techniques - K-Fold Cross Validation1 lessons
  1. 1Algorithm Evaluation Techniques - K-Fold Cross Validation
Algorithm Evaluation Techniques - Leave One Out Cross Validation1 lessons
  1. 1Algorithm Evaluation Techniques - Leave One Out Cross Validation
Algorithm Evaluation Techniques - Repeated Random Test-Train Splits1 lessons
  1. 1Algorithm Evaluation Techniques - Repeated Random Test-Train Splits
Algorithm Evaluation Metrics - Introduction1 lessons
  1. 1Algorithm Evaluation Metrics - Introduction
Algorithm Evaluation Metrics - Classification Accuracy1 lessons
  1. 1Algorithm Evaluation Metrics - Classification Accuracy
Algorithm Evaluation Metrics - Log Loss1 lessons
  1. 1Algorithm Evaluation Metrics - Log Loss
Algorithm Evaluation Metrics - Area Under ROC Curve1 lessons
  1. 1Algorithm Evaluation Metrics - Area Under ROC Curve
Algorithm Evaluation Metrics - Confusion Matrix1 lessons
  1. 1Algorithm Evaluation Metrics - Confusion Matrix
Algorithm Evaluation Metrics - Classification Report1 lessons
  1. 1Algorithm Evaluation Metrics - Classification Report
Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction1 lessons
  1. 1Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction
Algorithm Evaluation Metrics - Mean Absolute Error1 lessons
  1. 1Algorithm Evaluation Metrics - Mean Absolute Error
Algorithm Evaluation Metrics - Mean Square Error1 lessons
  1. 1Algorithm Evaluation Metrics - Mean Square Error
Algorithm Evaluation Metrics - R Squared1 lessons
  1. 1Algorithm Evaluation Metrics - R Squared
Classification Algorithm Spot Check - Logistic Regression1 lessons
  1. 1Classification Algorithm Spot Check - Logistic Regression
Classification Algorithm Spot Check - Linear Discriminant Analysis1 lessons
  1. 1Classification Algorithm Spot Check - Linear Discriminant Analysis
Classification Algorithm Spot Check - K-Nearest Neighbors1 lessons
  1. 1Classification Algorithm Spot Check - K-Nearest Neighbors
Classification Algorithm Spot Check - Naive Bayes1 lessons
  1. 1Classification Algorithm Spot Check - Naive Bayes
Classification Algorithm Spot Check - CART1 lessons
  1. 1Classification Algorithm Spot Check - CART
Classification Algorithm Spot Check - Support Vector Machines1 lessons
  1. 1Classification Algorithm Spot Check - Support Vector Machines
Regression Algorithm Spot Check - Linear Regression1 lessons
  1. 1Regression Algorithm Spot Check - Linear Regression
Regression Algorithm Spot Check - Ridge Regression1 lessons
  1. 1Regression Algorithm Spot Check - Ridge Regression
Regression Algorithm Spot Check - Lasso Linear Regression1 lessons
  1. 1Regression Algorithm Spot Check - Lasso Linear Regression
Regression Algorithm Spot Check - Elastic Net Regression1 lessons
  1. 1Regression Algorithm Spot Check - Elastic Net Regression
Regression Algorithm Spot Check - K-Nearest Neighbors1 lessons
  1. 1Regression Algorithm Spot Check - K-Nearest Neighbors
Regression Algorithm Spot Check - CART1 lessons
  1. 1Regression Algorithm Spot Check - CART
Regression Algorithm Spot Check - Support Vector Machines (SVM)1 lessons
  1. 1Regression Algorithm Spot Check - Support Vector Machines (SVM)
Compare Algorithms - Part 1 : Choosing the best Machine Learning Model1 lessons
  1. 1Compare Algorithms - Part 1 : Choosing the best Machine Learning Model
Compare Algorithms - Part 2 : Choosing the best Machine Learning Model1 lessons
  1. 1Compare Algorithms - Part 2 : Choosing the best Machine Learning Model
Pipelines : Data Preparation and Data Modelling1 lessons
  1. 1Pipelines : Data Preparation and Data Modelling
Pipelines : Feature Selection and Data Modelling1 lessons
  1. 1Pipelines : Feature Selection and Data Modelling
Performance Improvement: Ensembles - Voting1 lessons
  1. 1Performance Improvement: Ensembles - Voting
Performance Improvement: Ensembles - Bagging1 lessons
  1. 1Performance Improvement: Ensembles - Bagging
Performance Improvement: Ensembles - Boosting1 lessons
  1. 1Performance Improvement: Ensembles - Boosting
Performance Improvement: Parameter Tuning using Grid Search1 lessons
  1. 1Performance Improvement: Parameter Tuning using Grid Search
Performance Improvement: Parameter Tuning using Random Search1 lessons
  1. 1Performance Improvement: Parameter Tuning using Random Search
Export, Save and Load Machine Learning Models : Pickle1 lessons
  1. 1Export, Save and Load Machine Learning Models : Pickle
Export, Save and Load Machine Learning Models : Joblib1 lessons
  1. 1Export, Save and Load Machine Learning Models : Joblib
Finalizing a Model - Introduction and Steps1 lessons
  1. 1Finalizing a Model - Introduction and Steps
Finalizing a Classification Model - The Pima Indian Diabetes Dataset1 lessons
  1. 1Finalizing a Classification Model - The Pima Indian Diabetes Dataset
Quick Session: Imbalanced Data Set - Issue Overview and Steps1 lessons
  1. 1Quick Session: Imbalanced Data Set - Issue Overview and Steps
Iris Dataset : Finalizing Multi-Class Dataset1 lessons
  1. 1Iris Dataset : Finalizing Multi-Class Dataset
Finalizing a Regression Model - The Boston Housing Price Dataset1 lessons
  1. 1Finalizing a Regression Model - The Boston Housing Price Dataset
Real-time Predictions: Using the Pima Indian Diabetes Classification Model1 lessons
  1. 1Real-time Predictions: Using the Pima Indian Diabetes Classification Model
Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset1 lessons
  1. 1Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
Real-time Predictions: Using the Boston Housing Regression Model1 lessons
  1. 1Real-time Predictions: Using the Boston Housing Regression Model
Resources1 lessons
  1. 1Resources - Python for Machine Learning & Data Science

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Course Gate
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London
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