Artificial Intelligence (AI)

Course Duration - 6 Months Fee - ₹25,000/- ₹21,000/-

students learning AI at hartron faridabad
students learning AI at hartron faridabad
Futuristic AI training lab with dark neon lights and multiple computer screens
Futuristic AI training lab with dark neon lights and multiple computer screens

Artificial Intelligence (AI) is the future of innovation — powering everything from digital assistants to advanced data-driven systems.
At HARTRON Advanced Skill Centre, Ballabgarh, our AI course helps learners gain real-world knowledge in programming, machine learning, and data science through practical, project-based training.
This program is designed to develop your skills step-by-step, from Python programming to building intelligent systems used in modern industries.

What You’ll Learn

Module 1: Programming with Python

  • Basics of Python: syntax, variables, data types, and operators

  • Conditional statements, loops, and functions

  • Working with lists, dictionaries, and sets

  • File handling and exception management

  • AI Libraries: NumPy, Pandas, Matplotlib

Students learning Python programming in a modern computer classroom with instructor
Students learning Python programming in a modern computer classroom with instructor
Group of professionals working on data science dashboards and coding screens in a dim-lit room
Group of professionals working on data science dashboards and coding screens in a dim-lit room

Module 2: Conceptualising Data Science with Python

  • Relationship between AI, ML, and Data Science

  • Data collection, cleaning, and preprocessing techniques

  • Handling missing values and data outliers

  • Exploratory Data Analysis (EDA)

  • Feature scaling and encoding

Module 3: Data Analysis and Visualization

  • Data summarization and statistics

  • Data visualization using Matplotlib & Seaborn

  • Interactive dashboards with Power BI / Tableau

  • Case Study: Real-time data visualization

Students analyzing data and creating visualizations in a classroom with desktop systems
Students analyzing data and creating visualizations in a classroom with desktop systems
Team collaborating on machine learning algorithms with laptops displaying analytics
Team collaborating on machine learning algorithms with laptops displaying analytics

Module 4: Fundamentals of Machine Learning

  • Introduction to ML and its applications

  • Supervised & Unsupervised learning concepts

  • Regression models: Linear, Polynomial, Multiple

  • Classification algorithms: Decision Tree, KNN, SVM, Naive Bayes

  • Clustering: K-Means and Hierarchical clustering

  • Model evaluation: Accuracy, Precision, Recall, F1-score

Module 5: Performance and Accuracy of ML Models

  • Training and testing datasets

  • Cross-validation and performance tuning

  • Bias-variance tradeoff explanation

  • Hyperparameter tuning (Grid Search, Random Search)

  • Ensemble models (Bagging, Boosting, Random Forest)

Instructor explaining machine learning performance metrics to students in a modern lab
Instructor explaining machine learning performance metrics to students in a modern lab

NSQF level - 4.0 Eligibility - 12th Grade Pass