https://eclasess.com/python-with-ai-training-in-hyderabad.html

Module 1: Introduction to Python and AI

  • Lesson 1: Setting up Python Environment

    • Installing Python (latest version) and essential libraries (NumPy, pandas, scikit-learn, TensorFlow, Keras)

    • Introduction to IDE (e.g., VS Code, PyCharm) and Jupyter Notebooks

    • Basic Python syntax, data types, control flow (loops, conditionals)

  • Lesson 2: Python for Data Science

    • Data manipulation with pandas: DataFrames, Series

    • Data cleaning: handling missing values, duplicate removal

    • Basic data visualization using Matplotlib and Seaborn

  • Lesson 3: Basics of AI & Machine Learning

    • Understanding AI: Supervised vs. Unsupervised Learning

    • Overview of ML algorithms: Linear regression, Decision trees, Random forests

    • Introduction to Deep Learning and Neural Networks

Module 2: Understanding AI-Generated Content (AI-Gen)

  • Lesson 1: What is AI-Generated Content?

    • Introduction to NLP (Natural Language Processing) and its applications in AI content generation

    • Text generation using pre-trained models like GPT-3 and GPT-4

    • How AI can be used for content writing, summarization, and text-based recommendations

  • Lesson 2: Working with GPT Models

    • Introduction to OpenAI GPT models

    • Text generation techniques: temperature, max tokens, prompt engineering

    • Integrating OpenAI API to generate custom content such as articles, summaries, or reviews

  • Lesson 3: Automating Content Generation

    • Developing an AI-powered content generator using GPT models

    • Generating summaries for news articles, product descriptions, or social media posts

    • Incorporating user inputs (e.g., keywords, topics) to generate tailored content

Module 3: Web Scraping & APIs for Content Collection

  • Lesson 1: Web Scraping Basics

    • Introduction to web scraping: What it is and when to use it

    • Using BeautifulSoup, requests, and Selenium for scraping static and dynamic web pages

    • HTML/CSS parsing to extract specific content from websites like news sites, blogs, or forums

  • Lesson 2: Working with Social Media & Bookmarking APIs

    • Reddit API, Twitter API, and Pinterest API to collect content

    • Authentication using OAuth

    • Extracting and analyzing user interactions: posts, comments, bookmarks, likes, etc.

  • Lesson 3: Collecting & Preprocessing Content

    • Scraping news articles, product reviews, and social media content

    • Cleaning scraped data: Removing unwanted HTML tags, stopwords, and non-relevant content

    • Storing data in Databases like SQLite, MongoDB, or PostgreSQL

Module 4: Natural Language Processing (NLP) for Content Generation

  • Lesson 1: Basics of NLP

    • Tokenization, lemmatization, stemming

    • Removing stopwords and handling part-of-speech tagging

    • Introduction to TF-IDF (Term Frequency-Inverse Document Frequency) for text analysis

  • Lesson 2: Building NLP Models for Content Generation

    • Using pre-trained models like spaCy for entity recognition, text classification, and sentiment analysis

    • Text summarization techniques: extractive vs. abstractive summarization

    • Word embeddings: Understanding Word2Vec, GloVe, and fastText

  • Lesson 3: Fine-Tuning AI Models

    • Fine-tuning models like GPT-2 or BERT for specific use cases (e.g., product descriptions, blog writing)

    • Exploring transfer learning and domain adaptation

    • Using transformers library for AI-based content generation

Module 5: Implementing AI in Web Applications

  • Lesson 1: Building Web Applications with Python

    • Web frameworks: Flask or Django for building REST APIs and user interfaces

    • Integrating HTML and CSS with Flask/Django to create a user-friendly interface for content generation

    • Basic CRUD operations: Allow users to input topics and receive AI-generated content

  • Lesson 2: Creating an AI-Powered Content Generator

    • Design a content generator app where users can input topics, keywords, or styles for content

    • Integrate the OpenAI GPT-3 API or Transformers for dynamic content generation

    • Allow users to fine-tune generated content (e.g., tone, style)

  • Lesson 3: Deploying AI-Powered Web Apps

    • Deploy your content generation app on platforms like Heroku, AWS, or Google Cloud

    • Set up databases for storing user-generated content and AI output

    • Ensure scalability for high traffic

Module 6: Real-World Applications of AI-Generated Content

  • Lesson 1: Content Creation for Marketing & SEO

    • Using AI for creating SEO-friendly content: blog posts, product descriptions, landing pages

    • Automating social media posts using AI-generated text

    • Writing email newsletters with AI

  • Lesson 2: AI in Social Media & News

    • Generating personalized news and social media feeds

    • Using AI for headline generation, summaries, and engagement optimization

    • Integrating sentiment analysis for brand monitoring on social media

  • Lesson 3: Ethical Considerations in AI-Generated Content

    • Addressing bias in AI content generation models

    • Ensuring content authenticity and avoiding plagiarism

    • Ethical issues in using AI for content creation: misinformation, deepfakes

Module 7: Final Project and Assessment

  • Final Project: Build an AI-Powered Content Generation Platform

    • Create a full-stack application that integrates Python, AI models, and web frameworks

    • Allow users to input topics and receive automatically generated content in various formats (articles, summaries, social media posts)

    • Implement sentiment analysis and trending topic analysis in the generated content

  • Assessment & Evaluation

    • Present the final project to the class or instructor

    • Peer review and feedback on content generation features and app design

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