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: Data Analysis with Python Course Content 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 Data Preparation Fundamentals Introduction to data preparation Features vs target variable Train/test split Handling missing values Introduction to data quality Data Cleaning & Encoding Handling categorical variables Encoding techniques (label encoding, one-hot encoding) Identifying categorical vs numerical data Data transformation basics Feature Engineering Creating new features Extracting date-based features Binning and grouping data Log transformation Feature importance (concept) Data Preparation Pipeline Feature scaling (StandardScaler, MinMaxScaler) Feature selection (correlation, basic techniques) Building a clean data pipeline Preparing data for modelling Supervised Learning: Regression Introduction to regression Linear regression (concept and Python) Multiple linear regression Making predictions Model evaluation (MAE, MSE, R²) Visualising regression results 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 Decision Trees and Random Forest Concept and intuition Decision Tree in Python Controlling model complexity Random Forest in Python Comparing with Decision Trees Time Series & Forecasting Time-based data concepts Basic forecasting techniques Real-world business applications Unsupervised Learning: Clustering Introduction to clustering K-Means clustering (concept) K-Means in Python Choosing number of clusters (Elbow method) Business applications of clustering 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 Final Capstone Project End-to-end machine learning project Data preparation → modelling → evaluation → deployment Real-world business dataset Course Dates, Prices & Enrolment All Training Physical Classes Virtual Classes UTC British Time (UK, Ireland, Iceland) Central European Time (France, Germany, Sweden) Eastern European Time (Finland, Cyprus) Eastern Time (New York, Toronto, Montreal) Central Time (Chicago, Houston, Winnipeg) Mountain Time (Calgary, Denver, Edmonton) Mountain Time (Phoenix) Pacific Time (Los Angeles, Seattle, Vancouver) Singapore Time Arabic Standard Time (Qatar, Saudi Arabia) Gulf Standard Time (UAE, Oman) Australian Eastern Time (Sydney, Melbourne) Australian Central Time (Adelaide) Western Australia Time (Perth) New Zealand Time China Standard Time (China, Taiwan, Hong Kong) Scroll right for more details Delivery MethodDates & TimesHoursPriceEnrolment 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 Price Calculator & Booking Request Form Calculate prices for Corporate, 1-on-1 or group training and request a booking. Do you have a special training requirement or unable to find any suitable training date? Please complete and submit the booking request form, if you want to: book a course on different dates book for a group of delegates book corporate training book a customised training book a one-on-one training The price person is less when you book a course for more people. You can find the price per person and the total cost by changing the values of the training hours and the number of people below: How many hours? How many people? Total Cost Price per person Preferred Dates and Times Any other information
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