Explore the realm of Artificial Intelligence with our in-depth course, which is intended to provide you with the abilities and information needed to succeed in this fascinating profession. Our course provides the ideal balance of academic knowledge and real-world application, enabling you to become an expert in AI design, programming, and construction. Be at the forefront of technological innovation by enrolling in Top Al Training in Kerala course and training.

1. Algorithms
  • Machine learning algorithms. Without explicit programming, they allow computers to learn from data and gradually improve their abilities.
  • Algorithms for optimization: Used to optimize models and determine the appropriate settings for maximum performance.
2. Data
  • Training Data: Artificial intelligence models are train using large data sets. The success of AI systems depends on both the amount as well as the quality of input.
  • Data for Testing and Validation: Distinct databases are use to assess how well the models that were train function.
  • Labeled Data: In machine learning with supervision, data that has been annotate or categoriz to direct the learning process.
3. Training
  • Supervised Learning: The label dataset with input information and matching return is use for training the algorithm.
  • Unsupervised Learning: Without specific labels, the algorithm discovers links and trends in the information at hand.
  • Reinforcement Learning: Through interaction with the surroundings and feedback in the form of rewards or penalties, the trainee gains knowledge.
4. Ethical and Responsible AI:
  • The development and deployment of AI systems should consider ethical principles, transparency, accountability, and fairness to ensure the responsible use of AI technology

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  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