# Machine Learning

> Join Edoxi’s 40 to 60-hour online Python Machine Learning course to acquire essential skills for applying Python in data analysis and machine learning tasks.

## Course Details
- Rating: 4.9/5 (73 reviews)
- Category: Software & Technology
- Sub-Category: Emerging Technology

## Course Introduction
Edoxi’s 60 hour online Machine Learning course is designed to give you both a strong foundation and hands-on experience in Machine Learning using Python. Learn predictive modelling, Natural Language Processing (NLP), and deep learning techniques. Masters in Python libraries like Pandas, NumPy, and scikit-learn while working on real-world projects and case studies. Guided by industry experts, the course focuses on practical application, making you job-ready for roles in data science, AI, and software engineering. Enrol now to boost your tech career.

## Course Overview
- Delivery Modes: Online
- Course Duration: 60 Hours
- Corporate Days: 5 Days
- Learners Enrolled: 1000+
- Modules: 18

## What You’ll Learn from the Python Machine Learning Course

**Build a Strong Python Foundation**
You’ll start by learning Python basics like syntax, data types, and structures, so you can confidently move into machine learning.

**Understand Core Machine Learning Concepts**
You'll explore key ML topics such as regression, classification, and clustering, and learn how machines make decisions from data.

**Work with Real Data Using Pandas and NumPy**
You'll learn how to clean, prepare, and transform data using the most popular Python tools in data science.

**Build ML Models with Scikit-learn**
You’ll train and test machine learning models using Scikit-learn and gain an understanding of how they work in real-world projects.

**Create Clear and Useful Data Visuals**
You'll learn to use libraries like Matplotlib and Seaborn to turn your data into easy-to-read charts and graphs.

**Measure and Improve Model Accuracy**
You’ll learn how to check how well your model is working and make it better with performance metrics and testing techniques.

## About This Course
## About Our Python Machine Learning Course

Edoxi's 40–60 hour online Python Machine Learning Course is designed to build your career in Machine Learning or AI. 

Whether you're just starting or aiming to upskill, this course equips you with the essential skills to apply Python effectively in machine learning. During the course, you will learn about core machine learning concepts and build AI models. 

This flexible, expert-led course blends core theory with practical training to help you gain real-world experience in Machine Learning Using Python.

You'll begin with Python basics and move into core machine learning topics, including regression, classification, clustering, and deep learning. 

You'll also explore advanced areas like Natural Language Processing (NLP) and work with popular tools and libraries such as Scikit-learn, TensorFlow, Keras, Pandas, NumPy, and Jupyter Notebook.

The Python Machine Learning Training includes:

-

Step-by-step guidance through Python and machine learning concepts

- Hands-on coding sessions, live debugging, and case-based learning

- Real-world projects like predictive modelling and customer segmentation

- Flexible scheduling and interactive online support

- Preparation for roles in Data Science, Software Engineering, and AI Development

By the end of this Python Machine Learning Certification, you'll not only understand how to build and optimise ML models, but you’ll also have a job-ready portfolio to showcase your skills to employers in industries like tech, healthcare, finance, and e-commerce.

Start your journey with this globally recognised Python Machine Learning Course today. Enrol now and future-proof your career!

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## Key Features of Our Python Machine Learning Course

**All-in-One Curriculum**
You’ll learn everything from Python basics to Machine Learning, NLP, and Deep Learning– step by step, with real-world examples.

**Hands-On Coding Practice**
You’ll learn to use popular tools like Jupyter Notebook and Spyder to write, test, and debug your code with expert guidance throughout.

**Master Top Python Libraries**
You can get comfortable with essential data science tools like NumPy, Pandas, Matplotlib, and Seaborn for data handling and visualisation.

**Real Projects That Build Your Portfolio**
You may work on practical projects like price prediction and customer segmentation to show employers what you can do.

**Interactive and Engaging Learning**
You will join live coding sessions, group discussions, and hands-on labs using tools like TensorFlow and Scikit-learn.

**Flexible and Personalised Learning**
You’ll choose schedules that work for you– weekends, evenings, or fully online. The course is designed to fit around your life and goals.

## Who Can Join This Python Machine Learning Training?

**Beginners in Python & Machine Learning**
If you’re new to coding or ML, this course gives you a friendly, structured way to start from scratch.

**Students & Recent Graduates**
Perfect if you’ve got basic programming knowledge and want to explore a career in AI, Data Science, or Machine Learning.

**IT Professionals & System Admins**
Use your existing tech skills to move into machine learning, and learn how to build and deploy ML solutions.

**Software Developers & Engineers**
Ideal for developers looking to specialise in ML and integrate intelligent algorithms into applications.

**E-commerce & Finance Analysts**
If you work with large datasets, this course helps you apply ML for predictions, customer insights, and data-driven decisions.

**Cybersecurity Experts & Ethical Hackers**
Learn how to use ML techniques to detect threats, analyse risks, and improve security outcomes.

## Python Machine Learning Course Modules

### Module 1: Getting Started with Python

**Chapter 1.1: Introduction to Python**
- Lesson 1.1.1: What is Python and why it is widely used
- Lesson 1.1.2: Key features and benefits of Python
- Lesson 1.1.3: Setting up your Python environment

**Chapter 1.2: Python Basics**
- Lesson 1.2.1: Python syntax and program structure
- Lesson 1.2.2: Writing and running simple Python code

### Module 2: Variables, Data Types, and Operators

**Chapter 2.1: Variables and Data Types**
- Lesson 2.1.1: Using numbers, strings, and booleans
- Lesson 2.1.2: Type conversion and casting

**Chapter 2.2: Operators in Python**
- Lesson 2.2.1: Arithmetic and assignment operators
- Lesson 2.2.2: Logical and comparison operators

### Module 3: Control Flow and Loops

**Chapter 3.1: Conditional Statements**
- Lesson 3.1.1: If, else, and elif conditions
- Lesson 3.1.2: Using nested conditions

**Chapter 3.2: Loops in Python**
- Lesson 3.2.1: For and while loops
- Lesson 3.2.2: Break and continue statements

**Chapter 3.3: Looping Over Data**
- Lesson 3.3.1: Iterating through lists and collections

### Module 4: Functions and Modules

**Chapter 4.1: Functions in Python**
- Lesson 4.1.1: Creating and using functions
- Lesson 4.1.2: Parameters and return values
- Lesson 4.1.3: Scope and global variables

**Chapter 4.2: Python Modules**
- Lesson 4.2.1: Importing built-in modules
- Lesson 4.2.2: Creating and using custom modules

### Module 5: Strings and File Handling

**Chapter 5.1: String Operations**
- Lesson 5.1.1: Concatenation, slicing, and formatting

**Chapter 5.2: File Handling**
- Lesson 5.2.1: Reading and writing files
- Lesson 5.2.2: Opening, closing, and deleting files
- Lesson 5.2.3: File permissions and error handling

### Module 6: Data Structures

**Chapter 6.1: Basic Data Structures**
- Lesson 6.1.1: Lists, tuples, and dictionaries
- Lesson 6.1.2: Accessing and modifying values

**Chapter 6.2: Advanced Data Structures**
- Lesson 6.2.1: Using sets and frozensets

### Module 7: Object-Oriented Programming (OOP)

**Chapter 7.1: OOP Basics**
- Lesson 7.1.1: Classes, objects, and methods

**Chapter 7.2: Advanced OOP Concepts**
- Lesson 7.2.1: Encapsulation, inheritance, and polymorphism
- Lesson 7.2.2: Object interactions

### Module 8: File I/O and Serialization

**Chapter 8.1: File Input/Output**
- Lesson 8.1.1: Reading and writing files

**Chapter 8.2: Data Serialization**
- Lesson 8.2.1: Pickle, JSON, and CSV formats

### Module 9: Regular Expressions

**Chapter 9.1: Pattern Matching**
- Lesson 9.1.1: Regex basics
- Lesson 9.1.2: Validating and manipulating text

### Module 10: Python Libraries and Frameworks

**Chapter 10.1: Core Python Libraries**
- Lesson 10.1.1: Overview of NumPy, Pandas, and more

**Chapter 10.2: Application Areas**
- Lesson 10.2.1: Data analysis and scientific computing
- Lesson 10.2.2: Basics of web development with Python

### Module 11: Working with Data

**Chapter 11.1: Data Analysis with Pandas**
- Lesson 11.1.1: Introduction to dataframes
- Lesson 11.1.2: Manipulating and analysing data

**Chapter 11.2: Data Visualisation**
- Lesson 11.2.1: Creating visuals with Matplotlib

### Module 12: Introduction to Artificial Intelligence

**Chapter 12.1: AI Fundamentals**
- Lesson 12.1.1: What is AI and its history
- Lesson 12.1.2: Narrow AI vs General AI
- Lesson 12.1.3: AI applications in industries

### Module 13: Machine Learning Basics

**Chapter 13.1: Getting Started with ML**
- Lesson 13.1.1: Supervised vs unsupervised learning
- Lesson 13.1.2: Data preparation and preprocessing

**Chapter 13.2: Data Cleaning**
- Lesson 13.2.1: Handling missing and messy data
- Lesson 13.2.2: Normalisation and standardisation

### Module 14: Supervised Learning – Regression

**Chapter 14.1: Regression Models**
- Lesson 14.1.1: Linear and multiple regression
- Lesson 14.1.2: Polynomial and decision tree regression
- Lesson 14.1.3: Random forest regression

**Chapter 14.2: Model Evaluation**
- Lesson 14.2.1: Measuring and comparing regression models

### Module 15: Supervised Learning – Classification

**Chapter 15.1: Classification Techniques**
- Lesson 15.1.1: Logistic regression
- Lesson 15.1.2: K-nearest neighbours (k-NN)

**Chapter 15.2: Model Evaluation**
- Lesson 15.2.1: Techniques to assess classification models

### Module 16: Unsupervised Learning – Clustering

**Chapter 16.1: Clustering Methods**
- Lesson 16.1.1: Basics of clustering
- Lesson 16.1.2: k-means and hierarchical clustering

### Module 17: Natural Language Processing (NLP)

**Chapter 17.1: NLP Overview**
- Lesson 17.1.1: Types of NLP tasks
- Lesson 17.1.2: Implementing NLP with Python

### Module 18: Deep Learning

**Chapter 18.1: Neural Networks**
- Lesson 18.1.1: Basics of neural networks
- Lesson 18.1.2: Introduction to CNNs (Convolutional Neural Networks)

## Hands-On Lab Activities

**House Price Prediction Project**
Build a linear regression model to predict property prices based on location, size, and other features. A great way to apply supervised learning in a real estate scenario.

**Customer Behaviour Segmentation**
Use K-Means Clustering to group customers by shopping patterns and learn how unsupervised learning drives personalised marketing.

**Titanic Survival Prediction**
Apply logistic regression on the Titanic dataset to predict survival chances– a classic classification problem that teaches real-world data handling.

**Live Debugging & Code Walkthroughs**
Join interactive sessions where you’ll improve your code, learn to fix bugs, and optimise ML models with expert guidance.

**Group-Based ML Projects**
Collaborate with fellow learners to design, train, and refine machine learning models–mirroring how teams work in real jobs.

**Advanced Visualisation with Python**
Use Matplotlib and Seaborn to create stunning charts that explain your model's outcomes clearly and effectively.

**Regression Model Building**
Create and fine-tune models that predict results like sales, prices, or demand using Python libraries such as scikit-learn and Pandas.

**Classification Problem Solving**
Build smart models that categorise data, such as email spam detection or medical test results, using Python and ML algorithms.

## Python Machine Learning Course: Outcomes & Global Career Opportunities
Whether you're just starting or looking to upskill, this Python Machine Learning course is designed to take you from the basics to advanced concepts. By the end, you'll be ready to apply machine learning in real-world scenarios and pursue high-demand roles across industries.

- You’ll build a strong foundation in machine learning using Python – ideal for both beginners and professionals.
- You’ll learn to handle real datasets and create predictive models that drive business decisions.
- You’ll gain hands-on experience with popular tools like scikit-learn, NumPy, Pandas, and TensorFlow.
- You’ll be able to automate tasks, improve workflows, and create smart systems using data.
- You can apply for roles like Machine Learning Engineer, Data Scientist, AI Developer, or Data Analyst globally.
- You’ll become job-ready for opportunities in industries such as tech, finance, retail, healthcare, and logistics.

## Career Opportunities After the Python Machine Learning Course
Machine Learning Intern, Junior Data Scientist, Data Analyst, AI/ML Associate, Junior Machine Learning Engineer, Business Intelligence Analyst, Machine Learning Engineer, Data Scientist, Artificial Intelligence Specialist, Predictive Modeler, Data Engineer, NLP Engineer, Senior Machine Learning Engineer, Lead Data Scientist, AI/ML Architect, Principal Data Scientist, AI Research Scientist, Director of AI/ML Operations

## Machine Learning Using Python Training Options

**Live Online Training**
- 40-60 Hours of Live Online Training
- Flexible scheduling is available from Monday to Sunday.
- Real-time instructor interaction in a virtual learning environment.
- Digital course materials and AI tool tutorials provided.

**Corporate Training**
- Five-day intensive program tailored to organisational needs.
- Delivered in classroom or online formats as per preference.
- Real-world projects aligned with business objectives.
- Fly Me A Trainer option for tailored on-site training anywhere in the world
- Full logistics handled, including venue options (hotel, client premises, or our premises)
- Food and refreshments provided for corporate teams

## How To Get Python Machine Learning Certified?
Here’s a four-step guide to becoming a certified Machine Learning professional.

1. Enrol in Edoxi’s Python Machine Learning Course
2. Attend the 40-60 hours of Live Online Training
3. Complete Hands-on Projects and Post-Course Assessment
4. Get a Course Completion Certificate from Edoxi

## Why Choose Edoxi for Machine Learning Using Python Training
Whether you're a beginner or an experienced professional, Edoxi’s Machine Learning with Python course is designed to help you grow confidently in today’s data-driven world. Here’s why learners across the globe choose Edoxi:


**Beginner-Friendly Approach**
You’ll start with the basics of Python, learning key syntax and libraries before moving into machine learning. Therefore, no prior coding experience is required.

**Hands-On with Industry Tools**
You’ll get practical experience with tools like Jupyter Notebook, Spyder IDE, Scikit-learn, TensorFlow, Keras, Pandas, NumPy, Matplotlib, and Seaborn.

**Interactive and Personalised Learning**
You’ll participate in live coding sessions, debugging tasks, and receive one-on-one guidance to help you apply what you learn effectively.

**Real-World Projects for Your Portfolio**
You’ll work on industry-relevant projects like house price prediction and customer segmentation using real datasets to build a job-ready portfolio.

**Flexible Learning Options**
You can choose a learning schedule that fits your routine–perfect if you’re working or studying. You can receive additional support in Python and maths whenever needed.

**Course Completion Certificate**
You'll earn a Python Machine Learning Course completion certificate that enhances your career prospects in AI, Data Science, and Business Analytics.

## Frequently Asked Questions

**Q: Do I need any prerequisites to join the Python Machine Learning Course?**
A: Yes, basic knowledge of Python programming is recommended for this course. However, if you’re new to Python, don’t worry, we offer additional support classes to help you get started with Python basics, syntax, and key libraries.

While it's not mandatory, having a foundation in calculus, probability, or statistics can be helpful for understanding machine learning concepts more deeply. With or without prior knowledge, we’ll ensure you build the skills needed to succeed in the course.

**Q: How do I differentiate between Machine Learning, Deep Learning, and AI, and when should I use each?**
A: AI (Artificial Intelligence) is the broad concept of machines performing tasks like humans. Machine Learning (ML) is a subset of AI that trains models to make predictions based on data. Deep Learning (DL), a further subset of ML, uses neural networks for complex tasks like image recognition.

When to use: Use AI for automation, ML for predictions, and DL for intricate pattern recognition.

**Q: What are the key differences between TensorFlow and PyTorch, and how do I decide which to use?**
A: TensorFlow is ideal for deploying scalable, production-ready models, thanks to its robustness. PyTorch offers a more flexible, user-friendly environment, perfect for research and experimentation.

Choose TensorFlow when deploying apps at scale, and PyTorch for fast development and prototyping.

**Q: How do I handle imbalanced datasets in machine learning, especially for tasks like fraud detection?**
A: You can address the imbalance using techniques like SMOTE (oversampling), undersampling, or adjusting model weights. These methods help your model learn better from minority classes, improving accuracy for critical tasks like fraud detection.

**Q: What ethical considerations are important when building AI applications, especially in sensitive fields?**
A: When building AI applications, especially in sensitive fields, you need to prioritise data privacy, ensure fairness by mitigating biases, and maintain transparency in decision-making. Ethical AI builds trust and prevents harm, especially in healthcare, finance, and other sensitive areas.

## Trainer
- Name: Jothi Kumar
- Designation: Software and IT Trainer
Jothi is a Microsoft-certified technology specialist with more than 12 years of experience in software development for a broad range of industry applications. She has incomparable prowess in a vast grouping of software development tools like Microsoft Visual Basic, C#, .NET, SQL, XML, HTML, Core Java and Python.

Jothi has a keen eye for UNIX/LINUX-based technologies which form the backbone of all the free and open-source software movement. As a Big data expert, Jothi has experience using several components of the Hadoop ecosystem, including Hadoop Map Reduce, HDFS, HIVE, PIG, and HBase. She is well-versed in the latest technologies of information technology such as Data Analytics, Data Science and Machine Learning.

## Enrol in This Course
- Course URL: https://www.edoxi.com/machine-learning-course
- Phone: +971 43801666
- Email: info@edoxi.com