Machine Learning for Python Developers: From Fundamentals to Neural Networks

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Course Description

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

 

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 3: 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
 

Exam

N/A

Learning Path

N/A

Ways to Attend

• Attend a public course, check our schedule
• Private onsite Team training also available