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Deep Learning MasterClass

Deep Learning MasterClass

Learn about the Complete Life Cycle of a Deep Learning Project. Implement different Neural networks using Tensorflow & Keras.

What you’ll learn

Deep Learning MasterClass

  • You will learn the complete life cycle of a Data Science Project with Machine Learning and Deep Learning.

  • Learn about different Neural Networks like ANN, CNN, and RNN.

  • Learn about pandas, numpy, matplotlib, sklearn, and TensorFlow, some of the most important python libraries used in Data Science, ML, and DL.

  • You will build practical projects like Gold Price Prediction, Image Class Prediction, and Stock Price Prediction using different Neural


  • Basic understanding of Python Programming Language.


Deep learning is a subfield of machine learning 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, ” simply mathematical functions that process information. The layers in a deep neural network are organized hierarchically, 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. Still, the models can achieve state-of-the-art performance on various tasks, including image classification, natural language processing, speech recognition, and many others.

Different types of deep learning models exist, such as feedforward neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and many more. Each model type is suited for a different type of problem, and the choice of model will depend on the specific task and the available data type.


  • Complete the Life Cycle of the Data Science Project.

  • Important Data Science Libraries like Pandas, Numpy, Matplotlib, Seaborn, sklearn etc…

  • How to choose the 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…


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.

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