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  1. Algorithms:
    • Machine Learning Algorithms: These are the core of many AI systems. They enable machines to learn from data and improve their performance over time without being explicitly programmed.
    • Optimization Algorithms: Used for fine-tuning models and finding the optimal parameters to achieve the best performance.
  2. Data:
    • Training Data: Large datasets used to train machine learning models. The quality and quantity of data are crucial for the success of AI systems.
    • Testing and Validation Data: Separate datasets used to evaluate the performance of the trained models.
    • Labeled Data: Data that is annotated or categorized to guide the learning process in supervised machine learning.
  3. Models:
    • Machine Learning Models: These are mathematical representations of a system or process that AI systems use to make predictions or decisions. Examples include neural networks, decision trees, and support vector machines.
    • Deep Learning Models: A subset of machine learning that involves neural networks with multiple layers (deep neural networks). Deep learning has been particularly successful in tasks such as image and speech recognition.
  4. Training:
    • Supervised Learning: The model is trained on a labeled dataset where the input data and corresponding output are provided.
    • Unsupervised Learning: The model learns patterns and relationships within the data without explicit labels.
    • Reinforcement Learning: The model learns by interacting with an environment and receiving feedback in the form of rewards or penalties.
  5. Neural Networks:
    • Artificial Neural Networks (ANNs): Modeled after the human brain, ANNs consist of interconnected nodes organized into layers. Deep neural networks, with many hidden layers, are particularly effective in capturing complex patterns.
  6. Natural Language Processing (NLP):
    • Enables machines to understand, interpret, and generate human language. NLP is crucial for applications like chatbots, language translation, and sentiment analysis.
  7. Computer Vision:
    • Enables machines to interpret and make decisions based on visual data. It is used in image and video recognition, object detection, and autonomous vehicles.
  8. Speech Recognition:
    • Technology that converts spoken language into written text. It is used in virtual assistants, transcription services, and voice-activated systems.
  9. Robotics:
    • AI-powered robots use sensors and actuators to perceive and interact with the physical world. Robotics combines AI with mechanical engineering to create intelligent machines capable of performing tasks in various environments.
  10. Expert Systems:
    • Rule-based systems that mimic the decision-making ability of a human expert in a particular domain. They use a knowledge base of facts and rules to draw inferences and make decisions.
  11. Ethical and Responsible AI:
    • The development and deployment of AI systems should consider ethical principles, transparency, accountability, and fairness to ensure responsible use of AI technology.
  1. Introduction
    • What is Artificial Intelligence
    • Why Artificial Intelligence and Machine Learning
  2. Types of Machine Learning
    • Supervised learning – Classification and Regression
    • Unsupervised Learning
    • Reinforcement Learning
  3. Types of Machine Learning Problems
  4. Types of Data & Evaluation
  5. Modeling – Splitting Data, Tuning, Comparison
  6. Pandas: Data Analysis
    • Pandas Introduction
    • Series, Data Frames, and CSVs
    • Data from URLs
    • Describing Data with Pandas
    • Selecting and Viewing Data with Pandas and Data Manipulation
    • Practical Section: Data Manipulation with Pandas Exercises
  7. NumPy: Scientific Computing with Python
    • NumPy Introduction
    • NumPy DataTypes and Attributes
    • Creating NumPy Arrays
    • Operators in Numpy
    • Practical Section: NumPy Exercises and Applications
  8. Matplotlib: Plotting & Data Visualization
    • Matplotlib Introduction
    • Importing And Using Matplotlib
    • Data Visualizations
    • Plotting From Pandas DataFrames and Exercise
  9. Regular Expressions
  10. Data Engineering
    • Data Engineering Introduction
    • What Is Data and Data Engineering
    • Types Of Databases
    • Deep Learning and Unstructured Data
    • Visualizing Our Data
    • Summarizing and Evaluating Model
  11. Neural Networks
    • What is Neural Network and its use
  12. Natural Language Processing (NLP)
    • Introduction to NLP
    • Text preprocessing
    • Text classification
    • Named Entity Recognition (NER)
    • Sentiment analysis
  13. Feature Engineering
    • Importance of feature selection
    • Techniques for creating new features
    • Handling missing data
  14. Model Deployment and Productionisation
    • Deploying models to production
    • Model serving and APIs
    • Monitoring and maintaining deployed models
  15. Advanced Neural Networks
    • Convolutional Neural Networks (CNNs) for image data
    • Recurrent Neural Networks (RNNs) for sequential data
    • Transfer learning
  16. Time Series Analysis
    • Introduction to time series data
    • Time series forecasting
    • Seasonality and trend analysis
  17. Big Data and AI
    • Introduction to big data technologies (e.g., Hadoop, Spark)
    • Distributed computing for machine learning
  18. Capstone Project
    • Hands-on project to apply the knowledge gained throughout the course
    • Guidance and mentorship for the capstone project
  19. Recent Advancements in AI
    • Keeping up with the latest research and developments in AI
    • Emerging trends and technologies