Artificial Intelligence Course Overview

An Artificial Intelligence (AI) Course by INDIC Technologies equips learners with the knowledge and practical skills to build intelligent systems and automate decision-making using data-driven techniques. This course covers core concepts of AI, Machine Learning, Deep Learning, and Natural Language Processing (NLP), blending statistical theory with hands-on programming.

Programming & Tools

• Python & R Programming: Fundamentals of coding, data types, control structures, functions, and object-oriented programming.
• Data Handling: Data cleaning, transformation, and preprocessing using Pandas, NumPy, and data import techniques.
• Visualization: Create charts and visual data summaries using Matplotlib, Seaborn, and ggplot2.
• IDE & Editors: Jupyter Notebook, VS Code, RStudio for model development and experimentation.
• Version Control: Git & GitHub for tracking and managing code versions.

Mathematics & Statistics for AI

• Descriptive & Inferential Statistics: Central tendency, distributions, variance, correlation, and hypothesis testing.
• Probability & Outliers: Understand distributions, missing value treatment, and outlier detection using statistical measures.
• Error Metrics: Evaluation methods for regression and classification models such as MSE, RMSE, F1-score, and confusion matrix.

Supervised Learning:

• Linear & Logistic Regression: Predictive modeling techniques for continuous and categorical data.
• Decision Trees & Random Forests: Ensemble methods for improved accuracy and interpretability.
• Naïve Bayes & KNN: Probabilistic and distance-based algorithms for classification.

Unsupervised Learning:

• Clustering Techniques: K-means, hierarchical clustering to detect patterns in unlabelled data.

SVM:

• Support Vector Machines and kernel tricks for classification problems.

Artificial Intelligence Concepts

• Perceptron & Neural Networks: Basics of neural computation.
• Markov Decision Process: Modeling decision-making under uncertainty.
• First-Order Logic & Intelligent Agents: Fundamentals of knowledge representation and reasoning.
• AI Use Cases: Applications in robotics, recommendation engines, fraud detection, and more.

Deep Learning

• ANN (Artificial Neural Networks): Multilayer perceptrons and forward/backpropagation.
• CNN (Convolutional Neural Networks): Image classification and pattern recognition.
• RNN (Recurrent Neural Networks): Sequence modeling and time-series analysis.

Natural Language Processing (NLP)

• Text Preprocessing: Tokenization, stemming, lemmatization, noise removal.
• Feature Engineering: TF-IDF, word embeddings, n-grams, syntactic parsing.
• NLP Tasks: Text classification, sentiment analysis, named entity recognition (NER), and string matching.

Hands-on Learning

• Work on real-world projects involving text analysis, predictive modeling, and AI-powered applications.
• Build end-to-end solutions using real-time datasets from domains like e-commerce, finance, and social media.
• Deploy models and evaluate them in practical, business-relevant scenarios.