Deep Learning Mastery

Deep Learning Mastery, available at $54.99, has an average rating of 4.15, with 34 lectures, based on 257 reviews, and has 38362 subscribers.

You will learn about 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. This course is ideal for individuals who are Anyone who wants to get started with Deep Learning. or Data Science and ML folks who want to learn about Neural Networks and Deep Learning. It is particularly useful for Anyone who wants to get started with Deep Learning. or Data Science and ML folks who want to learn about Neural Networks and Deep Learning.

Enroll now: Deep Learning Mastery

Summary

Title: Deep Learning Mastery

Price: $54.99

Average Rating: 4.15

Number of Lectures: 34

Number of Published Lectures: 34

Number of Curriculum Items: 34

Number of Published Curriculum Objects: 34

Original Price: ₹999

Quality Status: approved

Status: Live

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.

Who Should Attend

Anyone who wants to get started with Deep Learning.
Data Science and ML folks who want to learn about Neural Networks and Deep Learning.

Target Audiences

Anyone who wants to get started with Deep Learning.
Data Science and ML folks who want to learn about Neural Networks and Deep Learning.

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 !!!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Numpy

Lecture 1: Introduction to Numpy

Lecture 2: Creating Arrays

Lecture 3: Shape and Reshape

Lecture 4: Indexing

Lecture 5: Iterating

Lecture 6: Slicing

Lecture 7: Searching and Sorting

Chapter 3: Pandas

Lecture 1: Introduction to Pandas

Lecture 2: Pandas Series

Lecture 3: DataFrame

Lecture 4: ReadCSV

Lecture 5: Analyze DataFrames

Chapter 4: Matplotlib and Seaborn for Data Visualization

Lecture 1: Introduction to Matplotlib

Lecture 2: Different Plots in Matplotlib

Lecture 3: Seaborn

Chapter 5: Machine Learning Fundamentals

Lecture 1: Machine Learning Introduction

Lecture 2: Supervised Machine Learning

Lecture 3: Unsupervised Machine Learning

Lecture 4: Train Test Split

Lecture 5: Machine Learning LifeCycle

Lecture 6: Working with Missing Values

Lecture 7: Feature Scaling

Lecture 8: Feature Encoding

Lecture 9: Model Evaluation Metrics

Chapter 6: Artificial Neural Networks (ANN)

Lecture 1: Introduction to Artificial Neural Networks (ANN)

Lecture 2: Activation Functions in Artificial Neural Networks

Lecture 3: Optimizers

Lecture 4: Gold Price Prediction using Artificial Neural Networks

Lecture 5: Diabetes Prediction using Artificial Neural Network

Chapter 7: Convolutional Neural Networks (CNN)

Lecture 1: CNN Introduction

Lecture 2: Implementation of CNN using Keras and Tensorflow

Chapter 8: Recurrent Neural Networks (RNN)

Lecture 1: RNN Introduction

Lecture 2: Microsoft Stock Price Prediction using LSTM

Instructors

Raj Chhabria
Computer Science Engineer with Specialization in DataScience

Rating Distribution

1 stars: 1 votes
2 stars: 10 votes
3 stars: 49 votes
4 stars: 95 votes
5 stars: 102 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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