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Data Science and Machine Learning with Python

Batch Price From £210 (approx. $284 USD) View Dates & Prices
Custom Price From £240 (approx. $325 USD) Price Calculator & Booking Short course on Data Science with Python
Total Duration: 12-Hour
Course level: Beginner to Intermediate
Delivery Method: Instructor-led Physical Classes or Virtual Classes
Certification: Certificate of Completion will be provided after completing the course

Course Overview

This course is designed to provide students with the knowledge and skills needed to perform data science using Python programming language. Students will learn to apply machine learning libraries such as Pandas, Seaborn and scikit-learn. The course will cover topics such as data cleaning, exploratory data analysis, machine learning algorithms, model evaluation, and selecting the appropriate model for business use.

Learning Objectives:

  • Understand the fundamental concepts of data science
  • Perform exploratory data analysis and visualization using Matplotlib and Seaborn libraries
  • Build predictive models using machine learning algorithms such as linear regression, logistic regression, decision trees, and random forests

Requirements

Basic programming knowledge in Python, familiarity with libraries like Numpy, Pandas, and Matplotlib.

We highly recommend you complete the following course(s) before attending the Data Science and Machine Learning with Python course:

You may also complete the following course(s) before attending the Data Science and Machine Learning with Python course but they are not mandatory:

Course Dates, Prices & Enrolment

All Training Physical Classes Virtual Classes
Time Zone:
Training MethodDates and TimesPrice 
Classroom Training 08 Sep 2025 - 10 Sep 2025
Mon, Tue & Wed
05:00 AM - 09:00 AM ET 		£420
(approx. $569 USD) 		Enrol Now

Course Content

  1. Introduction to Data Science in Python
    • Overview of data science
    • Introduction to Python for data science
  2. Exploratory Data Analysis and Feature Selection
    • Data visualization techniques
    • Data exploration with Pandas and Matplotlib
    • What are features and how are they important
  3. Introduction to Machine Learning in Python
    • Overview of machine learning
    • Introduction to Python for machine learning
    • Introduction to Google Colab Notebook
  4. Machine Learning Basics
    • Introduction of Overfitting and Underfitting
    • Class Imbalance and its solution
    • Download the dataset from Kaggle
    • Data splitting and data preparation for the Machine learning model
    • Machine learning Basics and Types
    • Supervised, Unsupervised, Reinforcement learning with example
  5. Supervised Learning Algorithms
    • Linear regression
    • Logistic regression
    • Decision trees and random forests
    • Project: Wine Quality Predictions
  6. Model Evaluation and Selection
    • Model evaluation metrics
    • Cross-validation
    • Model selection techniques
    • Ensemble Model
    • Model save and load
  7. Real-world Examples
    • Some real-life business applications
    • Q&A and course feedback
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