Certificate in Artificial Intelligence (AI) for Beginners

Your Pathway to Career in AI

This 6-month beginner-friendly program introduces the exciting world of Artificial Intelligence. It builds strong foundations in AI concepts, programming, machine learning, and modern tools like Generative AI, with a focus on practical, hands-on learning.

The course is ideal for students and freshers who want to start a career in the fast-growing AI field. No prior coding or technical background is required.

Course Details & Eligibility

Duration: 6 Months – 30 hours including classes, assignments, and projects. Can be delivered in hybrid mode: classroom + online sessions.)
Eligibility: 12th Pass (Any Stream) and above, Basic computer knowledge is sufficient; interest in technology is recommended.

Key Highlights

-Hands-on Learning: 15+ real-world projects and mini-projects
-Internship Provided: 1-2 month internship opportunity with industry partners or college-tied organizations (after completion of core modules)
-100% Job Assistance: Dedicated placement support, resume building, interview preparation, and connections with recruiters in AI/ML, data analysis, and tech roles
– Certification: Joint Certificate from S P More College upon successful completion (with attendance and project requirements)
– Certification: Diploma Certificate from S P More College upon successful completion
– Tools & Technologies: Python, TensorFlow/Keras, Scikit-learn, Power BI/Tableau, Hugging Face, industry-specific simulation tools
– Mode: Live classes + recorded sessions + practical labs + guest lectures from industry experts

Course Structure (Month-wise)

Months 1:Foundations of AI and Programming
– Introduction to Artificial Intelligence: What is AI, history, types (Narrow AI, General AI, Generative AI), real-world applications
– Python Libraries for AI: NumPy, Pandas, Matplotlib/Seaborn for data handling and visualization
-Introduction to Data: Types of data, basic statistics, data collection and cleaning
– Mini Project: Simple data analysis and visualization dashboard

Months 2: Mathematics for AI & Machine Learning Basics
– Essential Math: Linear algebra, probability, statistics, calculus basics (explained intuitively with examples)
– Introduction to Machine Learning: Supervised vs Unsupervised learning
– Regression and Classification algorithms: Linear Regression, Logistic Regression, Decision Trees
– Model evaluation: Accuracy, precision, recall, confusion matrix, overfitting/underfitting
– Hands-on: Building your first ML models using Scikit-learn
– Mini Projects: Image/text-based applications

Months 3: Advanced Machine Learning & Data Handling
– Ensemble methods: Random Forest, Boosting techniques
– Clustering: K-Means, Hierarchical clustering
– Feature engineering, data preprocessing, handling imbalanced data
– Introduction to Natural Language Processing (NLP) basics: Text processing, sentiment analysis
– Mini Project: Customer sentiment analysis or spam email classifier

Months 4: Deep Learning and Neural Networks
– Introduction to Neural Networks: Perceptrons, activation functions, backpropagation
– Deep Learning frameworks: TensorFlow/Keras basics
– Convolutional Neural Networks (CNNs) for image data
– Recurrent Neural Networks (RNNs) and basics of sequence data
– Hands-on labs: Building image classifiers and simple text models
– Mini Project: Image recognition model (e.g., handwritten digit or object detection basics)

Months 5: Generative AI, Ethics & Modern AI Tools
– Generative AI fundamentals: Large Language Models (LLMs), ChatGPT-like systems, prompt engineering
– Computer Vision basics and applications
-AI Ethics, bias, responsible AI, societal impact
– AI in industry: Case studies in healthcare, finance, education, e-commerce
– Tools: Basic use of Hugging Face, Google Gemini or similar for practical tasks
– Mini Project: Build a simple chatbot or content generator using prompt techniques

Months 6: Capstone Project, Internship & Career Preparation
– Major Capstone Project: End-to-end AI solution (e.g., AI-powered recommendation system, predictive analytics tool, or generative application)
– Internship module: Practical exposure in real or simulated industry setting
– Soft skills: Resume building, LinkedIn profile, interview preparation, portfolio development
– Guest lectures from industry experts
– Final assessment and project presentation

Learning Outcomes

By the end of the course, students will be able to:
– Understand core AI and ML concepts and their real-world applications
– Write Python code for data analysis and build basic AI models
– Apply machine learning and deep learning techniques to solve problems
– Use Generative AI tools effectively and responsibly
– Develop a professional portfolio with multiple projects
– Prepare confidently for entry-level roles such as AI Assistant, Junior ML Engineer, Data Analyst (AI focus), Prompt Engineer, or AI Content Specialist

Why Choose This Diploma at S P More College?

Affordable and accessible for beginners
– Strong practical focus with industry-relevant skills
– Direct pathway to internship and job assistance
– Supportive learning environment with experienced faculty

**New Batch Starting Soon!** Limited seats available.