Deep Learning Mastery

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  • Reading time:6 mins read


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

What you will learn

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, tensorflow that are 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 networks.

Description

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.


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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 !!!

English
language

Content

Introduction

Introduction

Numpy

Introduction to Numpy
Creating Arrays
Shape and Reshape
Indexing
Iterating
Slicing
Searching and Sorting

Pandas

Introduction to Pandas
Pandas Series
DataFrame
ReadCSV
Analyze DataFrames

Matplotlib and Seaborn for Data Visualization

Introduction to Matplotlib
Different Plots in Matplotlib
Seaborn

Machine Learning Fundamentals

Machine Learning Introduction
Supervised Machine Learning
Unsupervised Machine Learning
Train Test Split
Machine Learning LifeCycle
Working with Missing Values
Feature Scaling
Feature Encoding
Model Evaluation Metrics

Artificial Neural Networks (ANN)

Introduction to Artificial Neural Networks (ANN)
Activation Functions in Artificial Neural Networks
Optimizers
Gold Price Prediction using Artificial Neural Networks
Diabetes Prediction using Artificial Neural Network

Convolutional Neural Networks (CNN)

CNN Introduction
Implementation of CNN using Keras and Tensorflow

Recurrent Neural Networks (RNN)

RNN Introduction
Microsoft Stock Price Prediction using LSTM
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