Keras- Practical AI Projects Deep Learning using Keras

Keras: Practical AI Projects & Deep Learning using Keras, available at $54.99, has an average rating of 4, with 73 lectures, based on 2 reviews, and has 2273 subscribers.

You will learn about Building chatbots using Keras. Sentiment analysis implementation with recurrent neural networks (RNN). Image classification techniques using Keras. Advanced face recognition applications using computer vision and deep learning. Practical project implementation on Google Colab. Text preprocessing techniques like Bow Model, Count Vectorizer, Stemming, and Lemmatization. Model training, evaluation, and prediction. Pretrained model utilization and fine-tuning. Image preprocessing, augmentation, and visualization. Face detection and recognition algorithms. Embedding generation and classification. Real-world implementation and testing of AI models. This course is ideal for individuals who are Students or professionals seeking to enhance their skills in machine learning and deep learning. or Data scientists looking to expand their knowledge in natural language processing (NLP) and computer vision. or Software engineers interested in developing advanced applications using Keras and TensorFlow. or Individuals aspiring to build chatbots, perform sentiment analysis, and work on image classification and face recognition projects. or Professionals seeking to advance their careers in artificial intelligence (AI) and deep learning-related roles. or Anyone with a keen interest in exploring advanced projects in the field of artificial intelligence and machine learning. It is particularly useful for Students or professionals seeking to enhance their skills in machine learning and deep learning. or Data scientists looking to expand their knowledge in natural language processing (NLP) and computer vision. or Software engineers interested in developing advanced applications using Keras and TensorFlow. or Individuals aspiring to build chatbots, perform sentiment analysis, and work on image classification and face recognition projects. or Professionals seeking to advance their careers in artificial intelligence (AI) and deep learning-related roles. or Anyone with a keen interest in exploring advanced projects in the field of artificial intelligence and machine learning.

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Summary

Title: Keras: Practical AI Projects & Deep Learning using Keras

Price: $54.99

Average Rating: 4

Number of Lectures: 73

Number of Published Lectures: 73

Number of Curriculum Items: 73

Number of Published Curriculum Objects: 73

Original Price: $99.99

Quality Status: approved

Status: Live

What You Will Learn

Building chatbots using Keras. Sentiment analysis implementation with recurrent neural networks (RNN).

Image classification techniques using Keras. Advanced face recognition applications using computer vision and deep learning.

Practical project implementation on Google Colab. Text preprocessing techniques like Bow Model, Count Vectorizer, Stemming, and Lemmatization.

Model training, evaluation, and prediction. Pretrained model utilization and fine-tuning. Image preprocessing, augmentation, and visualization.

Face detection and recognition algorithms. Embedding generation and classification. Real-world implementation and testing of AI models.

Who Should Attend

Students or professionals seeking to enhance their skills in machine learning and deep learning.

Data scientists looking to expand their knowledge in natural language processing (NLP) and computer vision.

Software engineers interested in developing advanced applications using Keras and TensorFlow.

Individuals aspiring to build chatbots, perform sentiment analysis, and work on image classification and face recognition projects.

Professionals seeking to advance their careers in artificial intelligence (AI) and deep learning-related roles.

Anyone with a keen interest in exploring advanced projects in the field of artificial intelligence and machine learning.

Target Audiences

Students or professionals seeking to enhance their skills in machine learning and deep learning.

Data scientists looking to expand their knowledge in natural language processing (NLP) and computer vision.

Software engineers interested in developing advanced applications using Keras and TensorFlow.

Individuals aspiring to build chatbots, perform sentiment analysis, and work on image classification and face recognition projects.

Professionals seeking to advance their careers in artificial intelligence (AI) and deep learning-related roles.

Anyone with a keen interest in exploring advanced projects in the field of artificial intelligence and machine learning.

Welcome to the comprehensive course on practical applications of deep learning with Keras! In this course, you will embark on an exciting journey through various projects aimed at developing practical skills in deep learning and neural networks using the Keras framework. Whether you’re a beginner looking to get started with deep learning or an experienced practitioner seeking to enhance your skills, this course offers something for everyone.

Throughout this course, you will dive into hands-on projects covering a wide range of topics, including building chatbots, sentiment analysis using recurrent neural networks (RNNs), image classification, and advanced face recognition computer vision applications. Each project is carefully designed to provide you with practical experience and insights into real-world applications of deep learning.

By the end of this course, you will have gained valuable experience in implementing deep learning models, understanding their underlying principles, and applying them to solve complex tasks. Whether you’re interested in natural language processing, computer vision, or any other domain, the skills you acquire in this course will be invaluable in your journey as a deep learning practitioner.

Get ready to unlock the full potential of deep learning with Keras and take your skills to the next level!

Section 1: Building A Chatbot with keras

In this section, students will embark on a practical journey of constructing a chatbot using Keras. They will begin with an introduction to the project’s objectives, followed by an exploration of foundational concepts such as the Bag of Words (BoW) model, Count Vectorizer, and techniques for handling text data. Through a series of progressive lectures, students will delve into preprocessing steps, feature limitation strategies, and essential text processing elements like stop words and stemming.

Section 2: Project On Keras: Sentimental Analysis Using RNN

In the second section, students will transition to another project focusing on sentiment analysis with Recurrent Neural Networks (RNNs) using Keras. They will be introduced to Google Colab for collaborative work and IMBD dataset for sentiment analysis. The section will cover topics such as padding sequences, basic and complex LSTM models, and training procedures, enabling students to gain practical experience in sentiment analysis.

Section 3: Project On Keras – Image Classification

Continuing the journey, students will move to image classification projects in this section. They will learn to set up Google Colab, download datasets, and employ pretrained models for image classification tasks. Topics covered will include intermediate layer visualization, model creation, image augmentation, and model evaluation techniques.

Section 4: Project On Keras – Creating An Advanced Face Recognition Computer Vision App

In the final section, students will engage in creating an advanced face recognition application using computer vision techniques with Keras. They will explore Convolutional Neural Networks (CNNs) for image processing, face detection using MTCNN, and building a classifier for face recognition. This section will culminate in a comprehensive understanding of implementing deep learning models for real-world applications.

Course Curriculum

Chapter 1: Building A Chatbot with keras

Lecture 1: Introduction to Project

Lecture 2: Bow Model

Lecture 3: Count Vectorizer

Lecture 4: Text Data

Lecture 5: Text Data Continue

Lecture 6: Limit Number of Features

Lecture 7: Stop Words

Lecture 8: Stemming

Lecture 9: Stemming Continue

Lecture 10: Lemmatization

Lecture 11: ML Model on Text Data

Lecture 12: TF-TF-IDF Vectorizer

Lecture 13: Spacy Word2Vec

Lecture 14: Requirements

Lecture 15: Hindson Implementation

Lecture 16: Hindson Implementation Continue

Lecture 17: Neural Networks

Lecture 18: Generative Chatbots Part 1

Lecture 19: Generative Chatbots Part 2

Lecture 20: Generative Chatbots Part 3

Lecture 21: Generative Chatbots Part 4

Lecture 22: Generative Chatbots Part 5

Lecture 23: Attentive Chatbots Part 1

Lecture 24: Attentive Chatbots Part 2

Lecture 25: Attentive Chatbots Part 3

Lecture 26: Advanced Chatbot

Lecture 27: Advanced Chatbot – Evaluation

Lecture 28: Conclusion

Chapter 2: Project On Keras: Sentimental Analysis Using RNN

Lecture 1: Introduction to Project

Lecture 2: Google Collab

Lecture 3: Downloading IMBD Dataset

Lecture 4: Padding Sequences

Lecture 5: Basic LSTM Model

Lecture 6: Training

Lecture 7: Plotting

Lecture 8: Predicting on Basic LSTM

Lecture 9: Complex LSTM Model with Training

Lecture 10: Prediction with Complex LSTM

Chapter 3: Project On Keras – Image Classification

Lecture 1: Introduction to Project

Lecture 2: Google Collab

Lecture 3: Uploading

Lecture 4: Downloading the Dataset

Lecture 5: Pretrained Model

Lecture 6: Intermediate Layer Visualization

Lecture 7: Model Creation and Image Augmentation

Lecture 8: Compiling and Training Model

Lecture 9: Loss Values

Lecture 10: Test Images and Visualization

Lecture 11: Retraining the Model

Chapter 4: Project On Keras – Creating An Advanced Face Recognition Computer Vision App

Lecture 1: Introduction to Course

Lecture 2: CNN for Image Processing

Lecture 3: Image Preprocessing

Lecture 4: Saving and Loading the Models

Lecture 5: Getting System Ready

Lecture 6: Reading the Image Data

Lecture 7: Detect Faces MTCNN

Lecture 8: Draw Bounding Box

Lecture 9: Draw Key points

Lecture 10: Apply on Group of Images

Lecture 11: Extract Faces from Image

Lecture 12: Face Detection Summary

Lecture 13: Face Recognition

Lecture 14: Fashion Dataset

Lecture 15: Load Faces

Lecture 16: Load Dataset from Folders

Lecture 17: Load Dataset from Folders Continue

Lecture 18: Generate Face Embeddings

Lecture 19: Face Embeddings

Lecture 20: Building Classifier on Embeddings

Lecture 21: Building Classifier on Embeddings Continue

Lecture 22: Testing for Real Implementation

Lecture 23: Use Keras DNN with Face net

Lecture 24: Conclusion

Instructors

EDUCBA Bridging the Gap
Learn real world skills online

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