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Data Science and Machine Learning with Python

A Beginner to Intermediate level 12-hour course delivered via Instructor-led Physical Classes or Virtual Classes.

Regular Price: £420
Batch Price: From £360   📅  View Dates & Prices

Group Booking Discount: From £240 per person  🧮 Calculate & Book

1-to-1 Training: £420  📋 Booking Request Form

Course Overview

The Data Science and Machine Learning with Python course is designed to equip learners with the practical skills needed to analyse data, build predictive models, and solve real-world problems using machine learning. This hands-on course focuses on the full data science workflow, from data preparation and feature engineering to model building, evaluation, and deployment.

You will learn how to apply key machine learning techniques such as regression, classification, and clustering using Python and Scikit-learn. The course also introduces essential concepts such as overfitting, model evaluation, and performance improvement. By the end of the course, you will be able to build, evaluate, and deploy machine learning models and confidently apply data science techniques in real-world business scenarios.


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:


Course Content

  1. Introduction to Data Science & Machine Learning
    • Data science lifecycle
    • Data science vs analytics vs engineering
    • Machine learning vs traditional programming
    • Types of machine learning (supervised, unsupervised)
    • Regression vs classification
    • Training vs testing data
    • Overfitting vs underfitting
  2. Data Preparation Fundamentals
    • Introduction to data preparation
    • Features vs target variable
    • Train/test split
    • Handling missing values
    • Introduction to data quality
  3. Data Cleaning & Encoding
    • Handling categorical variables
    • Encoding techniques (label encoding, one-hot encoding)
    • Identifying categorical vs numerical data
    • Data transformation basics
  4. Feature Engineering
    • Creating new features
    • Extracting date-based features
    • Binning and grouping data
    • Log transformation
    • Feature importance (concept)
  5. Data Preparation Pipeline
    • Feature scaling (StandardScaler, MinMaxScaler)
    • Feature selection (correlation, basic techniques)
    • Building a clean data pipeline
    • Preparing data for modelling
  6. Supervised Learning: Regression
    • Introduction to regression
    • Linear regression (concept and Python)
    • Multiple linear regression
    • Making predictions
    • Model evaluation (MAE, MSE, R²)
    • Visualising regression results
  7. Supervised Learning: Classification
    • Introduction to classification
    • Binary vs multi-class classification
    • Logistic regression (concept and Python)
    • Model evaluation (accuracy, precision, recall, confusion matrix)
    • Improving classification performance
  8. Decision Trees and Random Forest
    • Concept and intuition
    • Decision Tree in Python
    • Controlling model complexity
    • Random Forest in Python
    • Comparing with Decision Trees
  9. Time Series & Forecasting
    • Time-based data concepts
    • Basic forecasting techniques
    • Real-world business applications
  10. Unsupervised Learning: Clustering
    • Introduction to clustering
    • K-Means clustering (concept)
    • K-Means in Python
    • Choosing number of clusters (Elbow method)
    • Business applications of clustering
  11. Model Evaluation & Deployment
    • Why model evaluation matters
    • Choosing the right evaluation metric
    • Saving and loading models (pickle)
    • Building simple prediction workflows
    • Introduction to deployment concepts
  12. Final Capstone Project
    • End-to-end machine learning project
    • Data preparation → modelling → evaluation → deployment
    • Real-world business dataset


Course Dates, Prices & Enrolment

Scroll right for more details
Delivery MethodDates & TimesHoursPriceEnrolment
Online Training using Zoom
14 Mar 2026 - 09 May 2026
4 Saturdays
09:30 AM - 12:30 PM BT
12-hour over 4-day
£360 £420
Full
Online Training using Zoom
23 May 2026 - 06 Jun 2026
3 Saturdays
03:00 PM - 07:00 PM BT
12-hour over 3-day
£420
Enrol Now
Classroom Training
15 Jun 2026 - 19 Jun 2026
Mon, Wed & Fri
10:00 AM - 02:00 PM BT
12-hour over 3-day
£420
Enrol Now
Online Training using Zoom
15 Jun 2026 - 19 Jun 2026
Mon, Wed & Fri
10:00 AM - 02:00 PM BT
12-hour over 3-day
£360 £420
Enrol Now
Online Training using Zoom
06 Jul 2026 - 17 Jul 2026
Mon, Wed & Fri (2 wks)
03:00 PM - 05:00 PM BT
12-hour over 6-day
£360 £420
Enrol Now
Classroom Training
20 Jul 2026 - 21 Jul 2026
Monday to Tuesday
10:00 AM - 04:00 PM BT
12-hour over 2-day
£420
Enrol Now
Online Training using Zoom
20 Jul 2026 - 21 Jul 2026
Monday to Tuesday
10:00 AM - 04:00 PM BT
12-hour over 2-day
£420
Enrol Now
Online Training using Zoom
25 Jul 2026 - 08 Aug 2026
3 Saturdays
03:00 PM - 07:00 PM BT
12-hour over 3-day
£420
Enrol Now
Online Training using Zoom
03 Aug 2026 - 14 Aug 2026
Mon, Wed & Fri (2 wks)
03:00 PM - 05:00 PM BT
12-hour over 6-day
£360 £420
Enrol Now
Online Training using Zoom
17 Aug 2026 - 21 Aug 2026
Mon, Wed & Fri
10:00 AM - 02:00 PM BT
12-hour over 3-day
£360 £420
Enrol Now
Classroom Training
17 Aug 2026 - 21 Aug 2026
Mon, Wed & Fri
10:00 AM - 02:00 PM BT
12-hour over 3-day
£420
Enrol Now
Classroom Training
21 Sep 2026 - 22 Sep 2026
Monday to Tuesday
10:00 AM - 04:00 PM BT
12-hour over 2-day
£420
Enrol Now
Online Training using Zoom
21 Sep 2026 - 22 Sep 2026
Monday to Tuesday
10:00 AM - 04:00 PM BT
12-hour over 2-day
£420
Enrol Now

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At London Academy of IT, we provide instructor-led online and in-person IT training in Data Analytics, SQL, Python, Power BI, and more. Our cutting-edge courses are designed to boost performance and enhance employability, providing the competitive edge employers look for.

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