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Module 1: Artificial intelligence
- Definition, history, and key concepts.
- Industry trends & career pathways in AI.
- Pandas, TensorFlow library, Keras, & PyTorch.
- Narrow, General, & Superintelligence.
- Research and present industry use cases of AI.
Module 2: Artificial intelligence AI Introduction
- AI applications in daily life.
- Historical milestones in AI development.
- NNs, deep & reinforcement learning.
- AI, Machine Learning, & Deep Learning.
- Case studies of successful AI projections.
Module 3: AI Data Structures and Algorithms (DSA)
- Sorting, searching, and optimization.
- Python and R Scripting
- Lists, dictionaries, and arrays.
- Loops, conditionals, and functions in Python.
- Libraries for AI development like NumPy, Pandas, and Matplotlib also seaborn.
- Write and test basic algorithms in Python.
- Solve simple mathematical problems using Python libraries.
Module 4: Machine Learning & AI Courses
- Supervised, unsupervised, & reinforcement learning.
- Linear & logistic regression, decision trees, clustering.
- Overfitting & more.
- ML tools and environments are Jupyter Notebook and Google Colab.
- Build ML models to predict outcomes from datasets.
- Analyze the performance of models using metrics.
Module 5: Data Science and Handling Data
- Data collection methods and sources (APIs, web scraping).
- Data cleaning and preprocessing: Handling missing data, and normalization.
- Exploratory Data Analysis & Finding patterns or anomalies.
- Data visualization using Matplotlib, Seaborn, or Tableau.
- Introduction to big data and cloud storage solutions.
- Perform EDA on a given dataset.
- Create dashboards to visualize insights & analytics.
Module 6: overview of NLP
- Tokenization, stemming, lemmatization.
- Text classification & sentiment analysis.
- TF-IDF & Word2Vec.
- NLTK, SpaCy, Hugging Face.
- Chatbots, translation systems, text summarization.
- Build a chatbot using Python and NLTK.
- Conduct sentiment analysis on social media datasets.
Module 7: AI in robotics
- Perception, decision-making, and action.
- Robotics programming basics using Arduino & Raspberry Pi.
- Introduction to ROS.
- Object detection and recognition.
- Building a simple AI-powered robot using sensors and actuators.
- Design and program a basic AI-driven robotic system.
- Integrate a camera module for basic object detection.
Module 8: Glimpse: Revolutionizing Research with Generative AI
- Overview of generative models like GANs, VAEs, & transformers.
- Image generation, music composition, and content generation.
- DALL·E, ChatGPT, and other tools.
- Fine tuning generative models for tasks.
- Ethical considerations and limitations of generative AI.
- Generate images or text using a pre-trained model.
- Fine-tune a generative AI model for a niche application.
Module 9: Future of AI
- Bias, fairness, transparency, & accountability.
- AI regulations and legal frameworks globally.
- Understanding Quantum AI, edge AI, and autonomous systems.
- AI development & deployment practices.
- Create a report discussing ethical guidelines for a specific AI project.
Module 10: artificial intelligence ai training Real time Live Projects
- Overview and design an AI-based solution for a real-world problem.
- Performing data collection, cleaning of the data, and also preprocessing.
- Build, train, and evaluate an ML or NLP model.
- Integrate the model into a functional prototype or application.
- Document and present project findings and outcomes.
- Full development and deployment of an AI application.
Professional documentation and project presentation.
NLP Engineer, MLOps Engineer, DevOps Engineer, Cloud Engineer, AI Researcher, Java Software Engineer, Machine Learning Engineer, Data Scientist, AI Engineer, Deep Learning Engineer, Business Intelligence Analyst, Data Analyst, Computer Vision Engineer
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