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Foundation Of AI

  • Start Your AI Journey Here

    • Comprehensive AI fundamentals for beginners to build a strong foundation.

    What you'll learn

    • Introduction to AI concepts
    • Basic AI algorithms
    • Machine learning overview
    • Supervised vs. unsupervised learning
    • Practical examples of AI use
    • Neural network basics
    • Data preprocessing techniques
    • Current trends in AI
    • Ethical AI practices

    Course content

    • Introduction and Overview
      a. History of machine learning and current trends in industry
      b. What is machine learning : supervised and unsupervised learning
      c. What to expect and not to expect from machine learning possibilities
      d. Overview of Statistical Tools where this will be demonstrated (R by default)
    • Classification
      KNN Classifier
      a. KNN introduction
      b. Data Normalization and examples for KNN
      c. customer analysis on an online ads wether customer would purchase from the advert to understand buyer behavior
      Naïve Bayes,/b>
      a. Bayes Theorem
      b. Naïve Bayes Intuition
      c. MLE and its Application
      d. Naïve bayes with multiple feature analysis
      e. Text analysis classiffier built additional concepts of test analysis, word cloud with help of data set
      Decision trees
      a. Introduction to decision trees - simple and complex/multi-node
      b. Clustering : Principal component analysis, K-means clustering
      c. DBSCAN introduction, hierarchical clustering
    • Ensemble Methods
      Random Forest
      a. Ensemble Methods Introduction
      b. Features of Random Forests
      c. How random forests work
      d. he out-of-bag (oob) error estimate
      e. Variable importance; Gini importance; Interactions;
      f. Balancing prediction error ;Detecting novelties; A case study
      g. This data set is related with a mortgage loan and challenge is to predict approval status of loan (Approved/ Reject).
      SVM
      a. Support Vector Machine introduction and examples
      b. Linear Kernels, Cross validation, radial kernels (all with examples in R)
      c. Hyperplane
      d. creating a non linear classifier using data set
      Gradient boosting machines
      a. Introduction to bagging and boosting
      b. Understanding Underlining Mathematics
      c. Practical usage examples
    • Clustering
      a. K-Means Clustering
      b. Hierarchical Clustering
      c. Customer spending Analysis
    • Regression Analysis
      a. Introduction To Regression Intuition
      b. Support Vector Regression (SVR)
      c. Decision Tree Regression
      d. Random Forest Regression
      e. Random Forest Regression
      f. other application examples for the model
    • Neural networks
      a. Introduction to neural networks and training neural networks
      b. Error and gradient calculation, backpropagation
      c. Application of neural networks, deep learning
      d. Practical usage examples
      e. Building a credit profile
      f. Application of Multiple ML techniques for fraud detect

    Requirements

    • No prior experience required
    • Interest in technology and AI
    • Basic computer literacy
    • Eagerness to learn and explore AI concepts

    Description

    The Foundation of AI course is perfect for those who are new to the world of artificial intelligence and want to build a solid understanding of its core principles. This training provides a comprehensive overview of AI, covering essential concepts, algorithms, and types of machine learning. With step-by-step instruction, participants will learn how AI models are developed and evaluated, along with practical examples of their use in different industries. This course sets the stage for deeper AI learning by equipping you with the knowledge to explore more complex topics in the future. Participants will also be introduced to neural networks and data preprocessing techniques, allowing them to appreciate the inner workings of AI systems. Additionally, we emphasize the importance of understanding ethical practices in AI, ensuring that learners are aware of the responsible use of these powerful technologies. Whether you're a student, a professional seeking to upgrade your skills, or just someone interested in AI, this course is designed to provide an accessible, engaging introduction that will lay the foundation for your journey in the field of AI.

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