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AI Mastery: Python’s Odyssey in Artificial Intelligence

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AI Mastery: Python’s Odyssey in Artificial Intelligence, available at $19.99, has an average rating of 4.15, with 46 lectures, based on 12 reviews, and has 3578 subscribers.

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You will learn about Master Python Fundamentals: Develop a solid foundation in Python, covering essential syntax, data structures, and functions for effective AI programming. Understand NumPy and Data Manipulation: Dive into NumPy, mastering array manipulation, indexing, and selection, essential for efficient data processing in AI Data Visualization with Matplotlib and Seaborn: Learn to visualize data effectively using Matplotlib and Seaborn, gaining the skills to create impactful plots Intermediate AI Concepts: Explore the intricacies of AI, including bias-variance tradeoffs, model evolution, and the practical implementation of ML algo. Hands-on Scikit Learn Applications: Gain practical experience with Scikit Learn, loading, and visualizing data, implementing dimensionality reduction etc. Deep Dive into Machine Learning: Develop expertise in classifiers, statistical analysis, label encoding, and accuracy scoring, paving the way for advanced ML Integration of Keras, Pytorch, and Tensorflow: Explore the diverse methods offered by Keras, Pytorch, and Tensorflow, applying them to binary classification Practical Applications in Jupyter Notebook: Work on real-world scenarios using Jupyter Notebook, combining theoretical knowledge with hands-on coding Build Neural Networks and Text Classification Models: Construct neural networks, delve into text classification using convolutional neural networks (CNN) Advanced AI Techniques: Extend your capabilities to advanced AI techniques, including collaborative filtering and recommendation systems By the end of this course, students will have acquired a versatile skill set, blending Python proficiency with a deep understanding of artificial intelligence This course is ideal for individuals who are Python Enthusiasts: Individuals passionate about Python programming, looking to expand their skills into the realm of artificial intelligence. or Beginners in AI: Those new to artificial intelligence seeking a comprehensive introduction, with a focus on practical applications using Python. or Intermediate Learners: Individuals with some AI knowledge wanting to deepen their understanding and gain hands-on experience with machine learning and neural networks. or Data Science Aspirants: Students interested in leveraging Python for data science and machine learning applications to analyze and derive insights from data. or Programming Professionals: Developers and programmers aiming to transition into AI, using Python as a powerful tool for creating intelligent applications. or AI Enthusiasts: Anyone intrigued by the possibilities of artificial intelligence and eager to explore its intricacies through a Python-centric approach. or This course caters to a diverse audience, providing a structured pathway for both beginners and intermediate learners to master Python in the context of artificial intelligence, making it accessible and valuable for a wide range of individuals. It is particularly useful for Python Enthusiasts: Individuals passionate about Python programming, looking to expand their skills into the realm of artificial intelligence. or Beginners in AI: Those new to artificial intelligence seeking a comprehensive introduction, with a focus on practical applications using Python. or Intermediate Learners: Individuals with some AI knowledge wanting to deepen their understanding and gain hands-on experience with machine learning and neural networks. or Data Science Aspirants: Students interested in leveraging Python for data science and machine learning applications to analyze and derive insights from data. or Programming Professionals: Developers and programmers aiming to transition into AI, using Python as a powerful tool for creating intelligent applications. or AI Enthusiasts: Anyone intrigued by the possibilities of artificial intelligence and eager to explore its intricacies through a Python-centric approach. or This course caters to a diverse audience, providing a structured pathway for both beginners and intermediate learners to master Python in the context of artificial intelligence, making it accessible and valuable for a wide range of individuals.

Enroll now: AI Mastery: Python’s Odyssey in Artificial Intelligence

Summary

Title: AI Mastery: Python’s Odyssey in Artificial Intelligence

Price: $19.99

Average Rating: 4.15

Number of Lectures: 46

Number of Published Lectures: 46

Number of Curriculum Items: 46

Number of Published Curriculum Objects: 46

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

  • Master Python Fundamentals: Develop a solid foundation in Python, covering essential syntax, data structures, and functions for effective AI programming.
  • Understand NumPy and Data Manipulation: Dive into NumPy, mastering array manipulation, indexing, and selection, essential for efficient data processing in AI
  • Data Visualization with Matplotlib and Seaborn: Learn to visualize data effectively using Matplotlib and Seaborn, gaining the skills to create impactful plots
  • Intermediate AI Concepts: Explore the intricacies of AI, including bias-variance tradeoffs, model evolution, and the practical implementation of ML algo.
  • Hands-on Scikit Learn Applications: Gain practical experience with Scikit Learn, loading, and visualizing data, implementing dimensionality reduction etc.
  • Deep Dive into Machine Learning: Develop expertise in classifiers, statistical analysis, label encoding, and accuracy scoring, paving the way for advanced ML
  • Integration of Keras, Pytorch, and Tensorflow: Explore the diverse methods offered by Keras, Pytorch, and Tensorflow, applying them to binary classification
  • Practical Applications in Jupyter Notebook: Work on real-world scenarios using Jupyter Notebook, combining theoretical knowledge with hands-on coding
  • Build Neural Networks and Text Classification Models: Construct neural networks, delve into text classification using convolutional neural networks (CNN)
  • Advanced AI Techniques: Extend your capabilities to advanced AI techniques, including collaborative filtering and recommendation systems
  • By the end of this course, students will have acquired a versatile skill set, blending Python proficiency with a deep understanding of artificial intelligence

Who Should Attend

  • Python Enthusiasts: Individuals passionate about Python programming, looking to expand their skills into the realm of artificial intelligence.
  • Beginners in AI: Those new to artificial intelligence seeking a comprehensive introduction, with a focus on practical applications using Python.
  • Intermediate Learners: Individuals with some AI knowledge wanting to deepen their understanding and gain hands-on experience with machine learning and neural networks.
  • Data Science Aspirants: Students interested in leveraging Python for data science and machine learning applications to analyze and derive insights from data.
  • Programming Professionals: Developers and programmers aiming to transition into AI, using Python as a powerful tool for creating intelligent applications.
  • AI Enthusiasts: Anyone intrigued by the possibilities of artificial intelligence and eager to explore its intricacies through a Python-centric approach.
  • This course caters to a diverse audience, providing a structured pathway for both beginners and intermediate learners to master Python in the context of artificial intelligence, making it accessible and valuable for a wide range of individuals.

Target Audiences

  • Python Enthusiasts: Individuals passionate about Python programming, looking to expand their skills into the realm of artificial intelligence.
  • Beginners in AI: Those new to artificial intelligence seeking a comprehensive introduction, with a focus on practical applications using Python.
  • Intermediate Learners: Individuals with some AI knowledge wanting to deepen their understanding and gain hands-on experience with machine learning and neural networks.
  • Data Science Aspirants: Students interested in leveraging Python for data science and machine learning applications to analyze and derive insights from data.
  • Programming Professionals: Developers and programmers aiming to transition into AI, using Python as a powerful tool for creating intelligent applications.
  • AI Enthusiasts: Anyone intrigued by the possibilities of artificial intelligence and eager to explore its intricacies through a Python-centric approach.
  • This course caters to a diverse audience, providing a structured pathway for both beginners and intermediate learners to master Python in the context of artificial intelligence, making it accessible and valuable for a wide range of individuals.

Welcome to the exciting world of “AI Mastery: Python’s Odyssey in Artificial Intelligence.” This course is meticulously designed to take you on a journey from the fundamentals to the intricacies of artificial intelligence (AI) using the versatile Python programming language. Whether you’re a beginner eager to explore the basics or an intermediate learner aiming to deepen your understanding, this course offers a comprehensive and hands-on approach to AI.

Overview:

In this course, you’ll start with the essentials, including setting up your development environment with Anaconda Navigator and diving into the powerful capabilities of NumPy. As you progress, you’ll explore the visualization landscape with Python libraries such as Matplotlib and Seaborn, honing your skills in data representation and analysis.

Moving into the intermediate level, the course delves into the heart of machine learning. You’ll unravel the nuances of data processing, bias, and variance tradeoffs, setting the stage for advanced AI concepts. Practical implementation is emphasized through Scikit Learn, guiding you in loading and visualizing data effectively. Hands-on applications, including dimensionality reduction and model selection, provide a solid foundation for building machine learning expertise.

Throughout the course, you’ll navigate real-world scenarios using Jupyter Notebook, gaining practical experience and reinforcing your theoretical knowledge. From binary classification tasks to exploring diverse methods with Keras, Pytorch, and Tensorflow, you’ll be equipped with the skills to tackle AI challenges head-on.

This course is not just about learning concepts; it’s about applying them in a dynamic and interactive environment. Join us on this AI journey, where theory meets practice, and empower yourself with the skills to thrive in the evolving field of artificial intelligence. Let’s unlock the potential of Python in the realm of AI together!

Section 1: Artificial Intelligence With Python – Beginner Level

In this introductory section, participants will embark on their artificial intelligence journey. The course begins with a warm welcome and an overview of the curriculum. Following this, learners are guided through the essential process of downloading and setting up Anaconda Navigator, a powerful tool for Python development. The installation process is thoroughly explained, ensuring that students can seamlessly set up their environments.

Once the foundation is laid, the course delves into the usage of NumPy within Jupyter Notebooks. Participants will grasp fundamental concepts such as array functions, indexing, and selection, empowering them with the skills to manipulate data efficiently. The exploration extends to Python libraries dedicated to visualization, with a focus on Matplotlib and Seaborn. Students will master the art of plotting data and creating impactful scatter plots, gaining a solid understanding of data representation.

Section 2: Artificial Intelligence With Python – Advanced Level

Building on the beginner level, the advanced section elevates participants’ understanding of artificial intelligence and machine learning. The journey begins with an exploration of Python’s role in AI, followed by a deep dive into the fundamentals of machine learning. Concepts such as data processing, bias, variance tradeoff, and model evolution are elucidated, providing a comprehensive understanding of the theoretical underpinnings.

The practical implementation comes to life with the utilization of Scikit Learn, a powerful machine learning library. Participants learn how to load and visualize data effectively, ensuring a robust foundation for subsequent tasks. Dimensionality reduction and model selection techniques are introduced, preparing learners for hands-on applications. Various classifiers, including Neighbors Classifier and Multilayer Perceptron, are covered, allowing participants to develop expertise in different machine learning paradigms.

The section also includes explorations of statistical analysis, label encoding, and accuracy scoring. The integration of Keras, Pytorch, and Tensorflow introduces learners to diverse methods, with a focus on binary classification tasks. The course embraces an interactive approach through Jupyter Notebook, enabling participants to apply their knowledge in real-world scenarios.

In summary, the “AI Mastery: Python’s Odyssey in Artificial Intelligence” course provides a holistic learning experience, covering foundational concepts for beginners and advancing into intermediate-level applications. Participants will not only acquire theoretical knowledge but also gain practical skills through hands-on coding and real-world examples.

Course Curriculum

Chapter 1: Artificial Intelligence With Python – Beginner Level

Lecture 1: Introduction to Course

Lecture 2: Download Anaconda Navigator

Lecture 3: Set up and Installation

Lecture 4: Numpy in Jupyter Notebook

Lecture 5: Array Function

Lecture 6: Numpy indexing and Selection

Lecture 7: Filter Function

Lecture 8: Python Libraries for Visualization

Lecture 9: Python Libraries for Visualization Continued

Lecture 10: Matpotlib Library and its Users

Lecture 11: Matpotlib Library and its Users Continued

Lecture 12: Plotting of Data

Lecture 13: Seaborn Package for Visualization

Lecture 14: Seaborn Package for Visualization Continued

Lecture 15: Scatter Plots

Lecture 16: Scatter Plots Continued

Lecture 17: Seaborn Libraries and its Implication

Chapter 2: Artificial Intelligence With Python – Advanced Level

Lecture 1: Introduction to Course

Lecture 2: Python for AI

Lecture 3: What is Machin Learning

Lecture 4: Data Processing Effort

Lecture 5: What is Meaning of Bias

Lecture 6: Bias vs Variance Tradeoff

Lecture 7: Model Evolution

Lecture 8: Scikit Learn

Lecture 9: Loading the Data

Lecture 10: Checking the Visualization

Lecture 11: Predict

Lecture 12: Data Values

Lecture 13: Applying Dimensionality Reduction

Lecture 14: Model Selection

Lecture 15: Neighbors Classifier

Lecture 16: Accuracy of Classifier

Lecture 17: ML Classification Hindson

Lecture 18: Statistical Analysis of the Dataset

Lecture 19: Import Label Encoder

Lecture 20: Accuracy Score

Lecture 21: Multilayer Perceptron

Lecture 22: Multilayer Perceptron Continued

Lecture 23: Number of Clusters

Lecture 24: Multiple Method

Lecture 25: Keras-Pytorch and Tensorflow

Lecture 26: Working on Jupyter Notebook

Lecture 27: Binary Classification

Lecture 28: Use Markdown Headings

Lecture 29: Pyplot

Instructors

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  • 4 stars: 3 votes
  • 5 stars: 5 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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