Deep Learning MasterClass
- Basic understanding of Python Programming Language.
Deep learning is a subfield of machine learning that is focused on building neural networks with many layers, known as deep neural networks. These networks are typically composed of multiple layers of interconnected “neurons” or “units”, which are simple mathematical functions that process information. The layers in a deep neural network are organized in a hierarchical manner, with lower layers processing basic features and higher layers combining these features to represent more abstract concepts.
Deep learning models are trained using large amounts of data and powerful computational resources, such as graphics processing units (GPUs). Training deep learning models can be computationally intensive, but the models can achieve state-of-the-art performance on a wide range of tasks, including image classification, natural language processing, speech recognition, and many others.
There are different types of deep learning models, such as feedforward neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and many more. Each type of model is suited for a different type of problem, and the choice of model will depend on the specific task and the type of data that is available.
IN THIS COURSE YOU WILL LEARN :
- Complete Life Cycle of Data Science Project.
- Important Data Science Libraries like Pandas, Numpy, Matplotlib, Seaborn, sklearn etc…
- How to choose appropriate Machine Learning or Deep Learning Model for your project
- Machine Learning Fundamentals
- Regression and Classification in Machine Learning
- Artificial Neural Networks (ANN)
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Tensorflow and Keras
- Different projects like Gold Price Prediction, Stock Price Prediction, Image Classification etc…
ALL THE BEST !!!
Who this course is for:
- Anyone who wants to get started with Deep Learning.
- Data Science and ML folks who want to learn about Neural Networks and Deep Learning.