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Python for Deep Learning: Build Neural Networks in Python
1397 Students (37 Reviews)
Learn Python for deep learning, build neural networks, train models, analyse predictions, and develop essential skills for machine learning and artificial intelligence careers.
GET THIS COURSE AND 3000+ OTHERS FOR ONLY £49 PER YEAR. FIND OUT MORE
Overview
Python for Deep Learning: Build Neural Networks in Python introduces learners to core deep learning concepts and the process of developing neural networks with Python. The course builds an understanding of how computational models learn from data, identify patterns, generate predictions, and support modern artificial intelligence applications across various industries and technical fields.
The training covers neural network components, input and output layers, activation functions, weights, biases, training methods, loss functions, and model evaluation. Learners will also develop knowledge of preparing datasets, creating models, applying training algorithms, testing predictions, and assessing results. These concepts provide a useful foundation for understanding how deep learning systems are developed, tested, and improved for different purposes.
Online study provides convenient access to course materials, allowing individuals to fit learning around existing personal, academic, or professional commitments. The course is suitable for beginners, students, Python learners, aspiring data scientists, machine learning enthusiasts, software professionals, and individuals interested in artificial intelligence. It can also support those seeking to strengthen programming knowledge and prepare for further learning or career opportunities involving deep learning, neural networks, machine learning, and AI technologies.
Learning Outcomes
- Understand fundamental concepts behind deep learning.
- Explore neural networks using Python programming.
- Build basic neural networks with Python.
- Prepare datasets for neural network training.
- Train models and evaluate their performance.
- Apply activation functions within neural networks.
- Improve models through suitable parameter adjustments.
Why choose this course
- Affordable, Accessible, informative and well-structured study materials
- Earn a globally recognised accredited qualification
- Study in your own time, at your own pace, through any device
- Benefit from feedback through mock exams and multiple-choice assessments
- Excellent career advancement/career switch opportunities
- Get 24/7 help or advice from our email and live chat teams
Certification
Certificate of Achievement
After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post.
Who Is This Course For
This course is suitable for beginners, students, Python learners, aspiring data scientists, machine learning enthusiasts, AI enthusiasts, software professionals, and anyone interested in building neural networks. It can also benefit individuals who want to understand how Python supports deep learning, model training, prediction, and artificial intelligence development.
Requirements
There are no formal academic entry requirements for this course. Learners should have basic computer skills and a reasonable understanding of English. Some familiarity with Python or basic programming concepts can be helpful, although beginners with a strong interest in deep learning and artificial intelligence can also study the course.
Career Paths
- Machine Learning Engineer
- Data Scientist
- Deep Learning Engineer
- Artificial Intelligence Analyst
- Neural Network Engineer
Course Curriculum
| Introduction to Deep Learning | |||
| What is Deep Learning? | 00:03:00 | ||
| Course Materials | 00:00:00 | ||
| Why is Deep Learning Important? | 00:02:00 | ||
| Software and Frameworks | 00:01:00 | ||
| Artificial Neural Networks (ANN) | |||
| Introduction – ANN | 00:01:00 | ||
| Anatomy and function of neurons | 00:01:00 | ||
| An introduction to the neural network | 00:03:00 | ||
| The architecture of a neural network | 00:02:00 | ||
| Propagation of information in ANNs | |||
| Feed-forward and Back Propagation Networks | 00:01:00 | ||
| Backpropagation In Neural Networks | 00:01:00 | ||
| Minimising the cost function using backpropagation | 00:01:00 | ||
| Neural Network Architectures | |||
| Single-layer perceptron (SLP) model | 00:01:00 | ||
| Radial Basis Network (RBN) | 00:01:00 | ||
| Multi-layer perceptron (MLP) Neural Network | 00:01:00 | ||
| Recurrent neural network (RNN) | 00:01:00 | ||
| Long Short-Term Memory (LSTM) networks | 00:02:00 | ||
| Hopfield neural network | 00:01:00 | ||
| Boltzmann Machine Neural Network | 00:01:00 | ||
| Activation Functions | |||
| What is the Activation Function? | 00:02:00 | ||
| Important Terminologies | 00:01:00 | ||
| The sigmoid function | 00:02:00 | ||
| Hyperbolic tangent function | 00:01:00 | ||
| Softmax function | 00:01:00 | ||
| Rectified Linear Unit (ReLU) function | 00:01:00 | ||
| Leaky Rectified Linear Unit function | 00:01:00 | ||
| Gradient Descent Algorithm | |||
| What is Gradient Decent? | 00:02:00 | ||
| What is Stochastic Gradient Decent? | 00:02:00 | ||
| Gradient Decent vs Stochastic Gradient Decent | 00:01:00 | ||
| Summary Overview of Neural Networks | |||
| How do artificial neural networks work? | 00:04:00 | ||
| Advantages of Neural Networks | 00:01:00 | ||
| Disadvantages of Neural Networks | 00:01:00 | ||
| Applications of Neural Networks | 00:02:00 | ||
| Implementation of ANN in Python | |||
| Introduction | 00:04:00 | ||
| Exploring the dataset | 00:01:00 | ||
| Problem Statement | 00:01:00 | ||
| Data Pre-processing | 00:04:00 | ||
| Loading the dataset | 00:02:00 | ||
| Splitting the dataset into independent and dependent variables | 00:03:00 | ||
| Label encoding using scikit-learn | 00:05:00 | ||
| One-hot encoding using scikit-learn | 00:06:00 | ||
| Training and Test Sets: Splitting Data | 00:04:00 | ||
| Feature Scaling | 00:03:00 | ||
| Building the Artificial Neural Network | 00:02:00 | ||
| Adding the input layer and the first hidden layer | 00:03:00 | ||
| Adding the next hidden layer | 00:01:00 | ||
| Adding the output layer | 00:02:00 | ||
| Compiling the artificial neural network | 00:03:00 | ||
| Fitting the ANN model to the training set | 00:02:00 | ||
| Predicting the test set results | 00:04:00 | ||
| Convolutional Neural Networks (CNN) | |||
| Introduction | 00:01:00 | ||
| Components of convolutional neural networks | 00:01:00 | ||
| Convolution Layer | 00:03:00 | ||
| Pooling Layer | 00:02:00 | ||
| Fully connected Layer | 00:02:00 | ||
| Implementation of CNN in Python | |||
| Dataset | 00:01:00 | ||
| Importing Libraries | 00:02:00 | ||
| Building the CNN model | 00:12:00 | ||
| Accuracy of the model | 00:02:00 | ||
| Order your Certificates & Transcripts | |||
| Order your Certificates & Transcripts | 00:00:00 | ||
Original price was: £319.£25Current price is: £25. ex Vat
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Frequently asked questions
Can’t find the anwser you’re looking for ? Reach out to customer support team.
Python for Deep Learning focuses on using Python programming to understand deep learning concepts and develop neural network models for processing and analysing data.
Neural networks are computational models designed to recognise patterns in data and produce outputs such as predictions, classifications, or other useful results.
Previous Python knowledge can be beneficial but is not essential. Beginners with basic computer skills and an interest in programming can also develop their knowledge through the course.
Students, Python learners, aspiring data scientists, machine learning enthusiasts, AI enthusiasts, software professionals, and anyone interested in neural network development can benefit from this course.
You will learn about neural network components, layers, activation functions, weights, biases, training, loss functions, predictions, and model performance evaluation.
Yes. Python is widely used for deep learning because it supports powerful libraries and frameworks that help developers create, train, test, and evaluate neural network models.
The course can provide foundational knowledge useful for further study and development towards careers involving artificial intelligence, machine learning, data science, and deep learning.
For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime.
Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks.
We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase.
Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course.
If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.