Machine Learning with Python
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Master Machine Learning with Python: Build Intelligent Models, Analyze Data & Automate Insights
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AI overview
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This comprehensive course provides a hands-on introduction to machine learning using Python. Designed for aspiring data professionals, it blends theoretical foundations with practical application of industry-standard tools and techniques.
- Understand core machine learning principles and algorithms
- Preprocess and clean data effectively for machine learning
- Apply regression, classification, and clustering techniques
- Evaluate model performance using key metrics
- Work confidently with Python-based ML libraries and tools
- Suitable for learners with some prior experience in Python programming
- Ideal for those interested in roles like Data Analyst, ML Engineer, or Data Scientist
- Suitable for learners wanting flexibility
- Online via Reed Courses Learning On Demand with immediate start
- Skills-based course designed to build practical machine learning knowledge
- Skills covered could be beneficial for data-centric projects and intelligent decision-making
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Overview
Certificates
Reed Courses Certificate of Completion
Digital certificate - Included
Will be downloadable when all lectures have been completed.
Curriculum
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Description
Machine Learning is at the heart of today’s data-driven decision-making. This course demystifies machine learning by teaching it through Python—a widely adopted programming language in the data science ecosystem. You’ll not only learn how ML models work but also gain the skills to build and evaluate them using real-world datasets.
Module Breakdown
Module 01: Foundations of Machine Learning
- What is Machine Learning?
- Types and Applications
- Python Basics for ML: Variables, Loops, Functions
- Jupyter Notebook & Anaconda Overview
- Installing Scikit-learn, Pandas, Matplotlib
Module 02: Data Preprocessing & Exploratory Analysis
- Understanding Datasets: Types and Formats
- Data Cleaning: Handling Missing Values, Removing Duplicates
- Feature Engineering Basics
- Exploratory Data Analysis (EDA) with Pandas & Seaborn
- Encoding, Normalisation, and Scaling Techniques
Module 03: Supervised Learning – Regression & Classification
- Linear Regression (Single & Multiple)
- Logistic Regression
- k-Nearest Neighbours (kNN)
- Decision Trees and Random Forests
- Model Training, Prediction, and Interpretation
Module 04: Model Evaluation & Tuning
- Train-Test Split and Cross Validation
- Confusion Matrix, Precision, Recall, F1 Score
- ROC-AUC Curve and Model Comparison
- Hyperparameter Tuning with GridSearchCV
- Avoiding Overfitting & Underfitting
Module 05: Unsupervised Learning & Dimensionality Reduction
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- Customer Segmentation, Pattern Detection
Career path
- Foundation for roles like Data Analyst, ML Engineer, or Data Scientist
- Helps businesses utilise data for intelligent decision-making
- Builds internal capabilities for data-centric projects
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This course is advertised on Reed.co.uk by the Course Provider, whose terms and conditions apply. Purchases are made directly from the Course Provider, and as such, content and materials are supplied by the Course Provider directly. Reed is acting as agent and not reseller in relation to this course. Reed's only responsibility is to facilitate your payment for the course. It is your responsibility to review and agree to the Course Provider's terms and conditions and satisfy yourself as to the suitability of the course you intend to purchase. Reed will not have any responsibility for the content of the course and/or associated materials.
