Machine Learning for Python Developers: From Fundamentals to Neural Networks

3 days

In today’s data-driven world, Machine Learning has become an indispensable tool for developers seeking to create intelligent, adaptive systems. This comprehensive, hands-on course is your gateway to mastering Machine Learning techniques using Python, one of the most versatile and powerful programming languages in the field.

Over three intensive days, you’ll embark on a journey through the Machine Learning landscape, gaining both theoretical knowledge and practical experience with core ML techniques. This course is not just about understanding algorithms; it’s about empowering you to solve real-world problems using cutting-edge tools and methodologies.

From the foundations of supervised and unsupervised learning to the intricacies of neural networks, you’ll develop a robust skill set that will set you apart in the competitive field of software development. By the end of this course, you’ll be equipped to harness the power of Machine Learning in your own projects, opening up new possibilities for innovation and efficiency in your work.

Whether you’re looking to enhance your current projects with predictive capabilities, transition into a data science role, or simply stay ahead of the curve in the rapidly evolving tech landscape, this course provides the knowledge and hands-on experience you need to succeed. Join us, and take the first step towards becoming a proficient Machine Learning practitioner.

Learning Objectives

By the end of this course, you will:

Have a solid understanding of the Machine Learning landscape
Be able to differentiate between various types of Machine Learning algorithms
Know how to identify suitable Machine Learning techniques for different types of data
Gain practical experience implementing and evaluating basic Machine Learning algorithms using Python libraries
Understand the fundamentals of Neural Networks and gain hands-on experience with PyTorch

Your Instructor

This course is led by Peter Munro, a veteran IT trainer and software developer with over 30 years of experience in the field. Peter’s extensive background spans across various domains of software development, with a particular interest in Machine Learning and its practical applications in industry. His teaching approach combines deep technical knowledge with real-world insights, ensuring that you not only understand the concepts but also learn how to apply them effectively in your projects.

Course Outline

Days 1 & 2: The Machine Learning Landscape

Module 1: Introduction to Machine Learning

Overview of key Machine Learning concepts and their real-world applications
Survey of Machine Learning tools and resources for Python developers
Setting up your Python environment for Machine Learning

Module 2: Regression - Your First Step into Supervised Learning

Understanding the principles of regression analysis
Implementing linear and polynomial regression using scikit-learn
Hands-on lab: Predicting house prices using regression techniques

Module 3: Feature Engineering and Selection

Techniques for effective feature transformation and scaling
Methods for selecting the most relevant features for your models
Lab: Improving model performance through feature engineering

Module 4: Unsupervised Learning with Clustering

Introduction to clustering algorithms (K-means, hierarchical clustering)
Applications and limitations of clustering techniques
Lab: Customer segmentation using clustering algorithms

Module 5: Classification - Predicting Categories

Overview of classification algorithms (Logistic Regression, Decision Trees)
Introduction to Naïve Bayes classification
Lab: Implementing a spam filter using Naïve Bayes

Module 6: Time Series Analysis

Fundamentals of time series data and its unique challenges
Techniques for analysing and forecasting time series data
Demonstration: Predicting stock prices using ARIMA models

Day 2: Neural Networks with PyTorch

Module 1: Fundamentals of Neural Networks

Introduction to artificial neural networks and their biological inspiration
Understanding the basic components: neurons, layers, activation functions
Overview of PyTorch and its ecosystem

Module 2: Building Your First Neural Network

Implementing a simple neural network for binary classification
Understanding and tuning hyperparameters
Lab: Predicting customer churn using a basic neural network

Module 3: Convolutional Neural Networks (CNNs)

Introduction to CNNs and their applications in image processing
Understanding convolutions, pooling, and fully connected layers
Lab: Building an image classification model using CNNs

Module 4: Recurrent Neural Networks (RNNs)

Introduction to RNNs and their use in sequence data
Understanding LSTM and GRU architectures
Lab: Implementing a sentiment analysis model using RNNs

Module 5: Close & Final Thoughts

Techniques for preventing overfitting (regularization, dropout)
Introduction to transfer learning and fine-tuning pre-trained models
Best practices for training and deploying neural networks

By the end of this course, you’ll have a solid foundation in Machine Learning concepts and practical experience implementing various algorithms using Python. You’ll be well-equipped to start applying these techniques to your own projects and continue your journey in the exciting field of Machine Learning.

Machine Learning is a vast and rapidly evolving field. This course provides you with the fundamentals and practical skills to get started, but continuous learning and practice are key to mastering Machine Learning. We encourage you to keep exploring, experimenting, and building upon the knowledge you’ve gained here.

Welcome to the world of Machine Learning. Your journey towards becoming a proficient ML practitioner starts here.