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AI Neural Insights: Deep Learning with Python

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AI Neural Insights: Deep Learning with Python, available at $19.99, has an average rating of 4, with 51 lectures, based on 8 reviews, and has 4689 subscribers.

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You will learn about Master Neural Networks: Gain a comprehensive understanding of neural networks, unraveling their intricacies and applications in artificial intelligence. Deep Learning Proficiency: Acquire hands-on experience in implementing deep learning techniques using Python, empowering to tackle real-world challenges Python Programming Mastery: Develop strong programming skills in Python, a versatile language crucial for implementing and experiment with deep learning algo. AI Applications: Explore practical applications of artificial intelligence, focusing on neural networks, and learn how to leverage them for various tasks Data Processing Techniques: Learn essential data preprocessing techniques, ensuring data is optimized and ready for effective deep learning model training. Advanced Model Creation: Dive into the creation of sophisticated neural network models, understanding various architectures and optimizing them Problem-Solving in AI: Develop critical problem-solving skills, enabling students to apply deep learning methodologies to diverse challenges and scenarios. Real-world Projects: Engage in hands-on projects, including applications of neural networks in cutting-edge domains, providing practical experience. Model Evaluation: Master techniques for evaluating the performance of deep learning models, ensuring students can assess and refine their creations effectively Stay Current with Trends: Stay abreast of the latest trends and advancements in artificial intelligence and deep learning, preparing students to adapt By the end of the course, students will emerge with a solid foundation in deep learning, practical Python skills, and the ability to apply neural insights This course is ideal for individuals who are Aspiring Data Scientists: Individuals looking to specialize in data science and artificial intelligence, seeking comprehensive knowledge in deep learning and Python programming. or Python Enthusiasts: Programmers and developers keen on expanding their skills to delve into the field of artificial intelligence, particularly focusing on neural networks. or AI Enthusiasts: Individuals passionate about artificial intelligence and eager to gain hands-on experience in deep learning with Python. or Students and Researchers: Academic individuals seeking to apply theoretical knowledge to practical scenarios, enhancing their understanding of neural networks and AI applications. or Tech Professionals Seeking Transition: Professionals in diverse industries interested in transitioning into roles related to artificial intelligence and deep learning, looking to acquire the necessary skills. or Intermediate Python Users: Individuals with intermediate Python programming skills wanting to elevate their capabilities by delving into the intricacies of deep learning. or Curious Innovators: Those curious about the latest trends and advancements in artificial intelligence, eager to explore and apply neural insights for innovative solutions. or Self-Learners: Enthusiastic learners who prefer self-paced education and are motivated to master deep learning using Python, regardless of their background or current skill level. or This course accommodates a diverse audience, providing a structured and engaging learning experience suitable for varying levels of expertise, from beginners to intermediate learners with a keen interest in AI and deep learning. It is particularly useful for Aspiring Data Scientists: Individuals looking to specialize in data science and artificial intelligence, seeking comprehensive knowledge in deep learning and Python programming. or Python Enthusiasts: Programmers and developers keen on expanding their skills to delve into the field of artificial intelligence, particularly focusing on neural networks. or AI Enthusiasts: Individuals passionate about artificial intelligence and eager to gain hands-on experience in deep learning with Python. or Students and Researchers: Academic individuals seeking to apply theoretical knowledge to practical scenarios, enhancing their understanding of neural networks and AI applications. or Tech Professionals Seeking Transition: Professionals in diverse industries interested in transitioning into roles related to artificial intelligence and deep learning, looking to acquire the necessary skills. or Intermediate Python Users: Individuals with intermediate Python programming skills wanting to elevate their capabilities by delving into the intricacies of deep learning. or Curious Innovators: Those curious about the latest trends and advancements in artificial intelligence, eager to explore and apply neural insights for innovative solutions. or Self-Learners: Enthusiastic learners who prefer self-paced education and are motivated to master deep learning using Python, regardless of their background or current skill level. or This course accommodates a diverse audience, providing a structured and engaging learning experience suitable for varying levels of expertise, from beginners to intermediate learners with a keen interest in AI and deep learning.

Enroll now: AI Neural Insights: Deep Learning with Python

Summary

Title: AI Neural Insights: Deep Learning with Python

Price: $19.99

Average Rating: 4

Number of Lectures: 51

Number of Published Lectures: 51

Number of Curriculum Items: 51

Number of Published Curriculum Objects: 51

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

  • Master Neural Networks: Gain a comprehensive understanding of neural networks, unraveling their intricacies and applications in artificial intelligence.
  • Deep Learning Proficiency: Acquire hands-on experience in implementing deep learning techniques using Python, empowering to tackle real-world challenges
  • Python Programming Mastery: Develop strong programming skills in Python, a versatile language crucial for implementing and experiment with deep learning algo.
  • AI Applications: Explore practical applications of artificial intelligence, focusing on neural networks, and learn how to leverage them for various tasks
  • Data Processing Techniques: Learn essential data preprocessing techniques, ensuring data is optimized and ready for effective deep learning model training.
  • Advanced Model Creation: Dive into the creation of sophisticated neural network models, understanding various architectures and optimizing them
  • Problem-Solving in AI: Develop critical problem-solving skills, enabling students to apply deep learning methodologies to diverse challenges and scenarios.
  • Real-world Projects: Engage in hands-on projects, including applications of neural networks in cutting-edge domains, providing practical experience.
  • Model Evaluation: Master techniques for evaluating the performance of deep learning models, ensuring students can assess and refine their creations effectively
  • Stay Current with Trends: Stay abreast of the latest trends and advancements in artificial intelligence and deep learning, preparing students to adapt
  • By the end of the course, students will emerge with a solid foundation in deep learning, practical Python skills, and the ability to apply neural insights

Who Should Attend

  • Aspiring Data Scientists: Individuals looking to specialize in data science and artificial intelligence, seeking comprehensive knowledge in deep learning and Python programming.
  • Python Enthusiasts: Programmers and developers keen on expanding their skills to delve into the field of artificial intelligence, particularly focusing on neural networks.
  • AI Enthusiasts: Individuals passionate about artificial intelligence and eager to gain hands-on experience in deep learning with Python.
  • Students and Researchers: Academic individuals seeking to apply theoretical knowledge to practical scenarios, enhancing their understanding of neural networks and AI applications.
  • Tech Professionals Seeking Transition: Professionals in diverse industries interested in transitioning into roles related to artificial intelligence and deep learning, looking to acquire the necessary skills.
  • Intermediate Python Users: Individuals with intermediate Python programming skills wanting to elevate their capabilities by delving into the intricacies of deep learning.
  • Curious Innovators: Those curious about the latest trends and advancements in artificial intelligence, eager to explore and apply neural insights for innovative solutions.
  • Self-Learners: Enthusiastic learners who prefer self-paced education and are motivated to master deep learning using Python, regardless of their background or current skill level.
  • This course accommodates a diverse audience, providing a structured and engaging learning experience suitable for varying levels of expertise, from beginners to intermediate learners with a keen interest in AI and deep learning.

Target Audiences

  • Aspiring Data Scientists: Individuals looking to specialize in data science and artificial intelligence, seeking comprehensive knowledge in deep learning and Python programming.
  • Python Enthusiasts: Programmers and developers keen on expanding their skills to delve into the field of artificial intelligence, particularly focusing on neural networks.
  • AI Enthusiasts: Individuals passionate about artificial intelligence and eager to gain hands-on experience in deep learning with Python.
  • Students and Researchers: Academic individuals seeking to apply theoretical knowledge to practical scenarios, enhancing their understanding of neural networks and AI applications.
  • Tech Professionals Seeking Transition: Professionals in diverse industries interested in transitioning into roles related to artificial intelligence and deep learning, looking to acquire the necessary skills.
  • Intermediate Python Users: Individuals with intermediate Python programming skills wanting to elevate their capabilities by delving into the intricacies of deep learning.
  • Curious Innovators: Those curious about the latest trends and advancements in artificial intelligence, eager to explore and apply neural insights for innovative solutions.
  • Self-Learners: Enthusiastic learners who prefer self-paced education and are motivated to master deep learning using Python, regardless of their background or current skill level.
  • This course accommodates a diverse audience, providing a structured and engaging learning experience suitable for varying levels of expertise, from beginners to intermediate learners with a keen interest in AI and deep learning.

Welcome to “Deep Learning Mastery with Python,” a transformative course designed to empower you with the knowledge and skills required to navigate the intricate world of deep learning using Python. This course is meticulously crafted to provide a comprehensive understanding of various deep learning concepts and practical applications, ensuring that you not only grasp the theoretical foundations but also gain hands-on experience through engaging projects.

Section 1: Deep Learning: Convolutional Neural Network CNN using Python

In this section, participants embark on a comprehensive exploration of Convolutional Neural Networks (CNN) using Python. The initial lectures introduce the project, providing a sneak peek into the objectives. Subsequent sessions delve into the essential elements, covering the installation process, dataset structure, and the intricacies of coding the CNN model and its layers. The focus expands to vital components like data preprocessing, augmentation, and understanding data generators, creating a solid foundation for practical implementation.

Section 2: Deep Learning: Artificial Neural Network ANN using Python

Transitioning to Artificial Neural Networks (ANN), this section initiates with an introduction to the project, followed by the setup of the environment for ANN development. The course proceeds to guide participants through the installation of necessary libraries and data preprocessing steps. Key topics include data exploration, encoding, and the meticulous preparation of datasets for training. The step-by-step construction of the ANN, spanning multiple lectures, ensures a comprehensive understanding, culminating in prediction processes and addressing data imbalance through resampling.

Section 3: Deep Learning: RNN, LSTM, Stock Price Prognostics using Python

This section commences with an introduction to a project centered on Stock Price Prognostics using Deep Learning. It covers the installation of required components and explores libraries vital for the task. Lectures then guide participants through dataset exploration, data preprocessing, and exploratory data analysis. The focus expands to feature scaling, building Recurrent Neural Networks (RNN), and the training and prediction processes. The section concludes with visualizing the final results, providing a holistic view of applying deep learning to stock price predictions.

Section 4: Deep Learning: Project using Convolutional Neural Network CNN in Python

The final section introduces a hands-on project utilizing Convolutional Neural Networks (CNN) in Python. Participants begin by understanding the project’s scope, followed by leveraging Google Colab for collaborative work. Lectures guide through importing packages and data, preprocessing steps, model creation, training, and prediction. The emphasis on visualization enhances the practicality of the project, ensuring participants gain a comprehensive understanding of applying CNN in a real-world scenario.

This course structure ensures a progressive and comprehensive journey through different facets of deep learning, providing participants with practical skills applicable to diverse applications. Whether you’re a novice exploring the fascinating field of deep learning or a seasoned professional aiming to enhance your skills, “Deep Learning Mastery with Python” promises to be your guide to mastering the complexities of deep learning and unleashing your potential in the world of artificial intelligence. Let’s dive in and unlock the power of deep learning together!

Course Curriculum

Chapter 1: Deep Learning: Convolutional Neural Network CNN using Python

Lecture 1: Introduction of Project

Lecture 2: Overview of CNN

Lecture 3: Installations and Dataset Structure

Lecture 4: Import libraries

Lecture 5: CNN Model and Layers Coding

Lecture 6: Data Preprocessing and Augmentation

Lecture 7: Understanding Data generator

Lecture 8: Prediction on Single Image

Lecture 9: Understanding Different Models and Accuracy

Chapter 2: Deep Learning: Artificial Neural Network ANN using Python

Lecture 1: Introduction of Project

Lecture 2: Setup Environment for ANN

Lecture 3: ANN Installation

Lecture 4: Import Libraries and Data Preprocessing

Lecture 5: Data Preprocessing

Lecture 6: Data Preprocessing Continue

Lecture 7: Data Exploration

Lecture 8: Encoding

Lecture 9: Encoding Continue

Lecture 10: Preparation of Dataset for Training

Lecture 11: Steps to Build ANN Part 1

Lecture 12: Steps to Build ANN Part 2

Lecture 13: Steps to Build ANN Part 3

Lecture 14: Steps to Build ANN Part 4

Lecture 15: Predictions

Lecture 16: Predictions Continue

Lecture 17: Resampling Data with Imbalance-Learn

Lecture 18: Resampling Data with Imbalance-Learn Continue

Chapter 3: Deep Learning: RNN, LSTM, Stock Price Prognostics using Python

Lecture 1: Introduction of Project

Lecture 2: Installation

Lecture 3: Libraries

Lecture 4: Dataset Explore

Lecture 5: Import Libraries

Lecture 6: Data Preprocessing

Lecture 7: Exploratory Data Analysis

Lecture 8: Exploratory Data Analysis Continue

Lecture 9: Feature Scaling

Lecture 10: Feature Scaling Continue

Lecture 11: More on Feature Scaling

Lecture 12: Building RNN

Lecture 13: Building RNN Continue

Lecture 14: Training of Network

Lecture 15: Prediction on Test Data

Lecture 16: Prediction on Test Data Continue

Lecture 17: Final Result Visualization

Chapter 4: Deep Learning: Project using Convolutional Neural Network CNN in Python

Lecture 1: Introduction to Project

Lecture 2: Google Collab

Lecture 3: Importing Packages and Data

Lecture 4: Preprocessing and Model Creation

Lecture 5: Training the Model and Prediction

Lecture 6: Model Creation using CNN

Lecture 7: CNN Model Prediction

Instructors

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  • 5 stars: 4 votes

Frequently Asked Questions


How long do I have access to the course materials?


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


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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