Enroll Course: https://www.udemy.com/course/deep-learning-neural-networks-python-keras-for-dummies/
Are you fascinated by the buzz around Machine Learning and Deep Learning, but intimidated by the perceived complexity? Do you want to build intelligent applications but feel like you need a PhD in mathematics first? Then Abhilash Nelson’s “Deep Learning & Neural Networks Python – Keras: For Dummies” on Udemy is the perfect course for you.
Nelson expertly demystifies deep learning, drawing an analogy between learning to drive a car and learning deep learning. Just as you don’t need to be an automotive engineer to drive, you don’t need to be a math wizard to build powerful neural networks. This course acts as your user-friendly control panel, guiding you through the essential concepts and Keras library functions to create efficient deep learning models.
The course kicks off with a solid theoretical foundation, covering the differences between Machine Learning and Deep Learning, the history of neural networks, their basic workflow, and the concepts of biological and artificial neurons. You’ll even get help deciding which approach is best for your AI projects.
Before diving into coding, Nelson ensures your environment is ready. You’ll learn to set up Python with Anaconda and install crucial libraries like Theano, TensorFlow, and Keras. For those new to Python, the course includes a helpful primer on the language’s basics, from syntax to data structures.
With the groundwork laid, the course transitions to practical implementation. You’ll explore Multi-Layer Perceptrons (MLPs) and the key steps in training a neural network. Then, you’ll get hands-on with real-world datasets:
* **Pima Indians Onset of Diabetes Dataset:** Build a classification model, train it, evaluate accuracy, and experiment with data splitting and K-Fold Cross-Validation.
* **Iris Flowers Classification Dataset:** Tackle multi-class classification with this popular dataset.
* **Sonar Returns Dataset:** Classify signals reflected by rocks or mines, and improve model performance through standardization and network topology adjustments.
* **Boston House Prices Dataset:** Shift gears to regression, predicting housing costs and optimizing the model.
The course doesn’t stop at model building. You’ll learn essential techniques like saving and loading trained models, making predictions on custom data, and implementing checkpointing to prevent loss of progress during long training sessions. You’ll also gain insights into visualizing model training history and combating overfitting with dropout regularization.
Furthermore, you’ll explore learning rate scheduling and delve into the powerful world of Convolutional Neural Networks (CNNs). The course includes practical CNN applications like handwritten digit recognition using the MNIST dataset and object recognition with the CIFAR-10 dataset. You’ll even learn techniques to enhance model performance through image augmentation and various transformations.
“Deep Learning & Neural Networks Python – Keras: For Dummies” is more than just a course; it’s a comprehensive journey from basic concepts to advanced applications. Nelson’s approach makes complex topics accessible, empowering you to skyrocket your career prospects in the rapidly growing field of AI. Upon completion, you’ll receive an experience certificate, a valuable addition to your professional portfolio. Prepare to enter the exciting realm of thinking machines!
Enroll Course: https://www.udemy.com/course/deep-learning-neural-networks-python-keras-for-dummies/