SukhNexus IT Solutions
Artificial Intelligence Training Program
Master AI, Machine Learning, Deep Learning, and Generative AI with practical tools, live projects, and career-focused mentorship.
11
Modules
12+
Projects
4.9★
Rating
Machine Learning + Deep Learning + GenAI
Hands-on Python, Models, and AI Tools
Portfolio Projects & Interview Preparation
Small Batches — Max 15 Students
About the Course
Course Description
The Artificial Intelligence Training Program at SukhNexus IT Solutions is designed for students, professionals, and tech enthusiasts who want to build strong foundations in AI and learn how intelligent systems work in the real world.
This course covers Python for AI, data handling, machine learning, deep learning, neural networks, computer vision basics, natural language processing, and the latest generative AI tools. With hands-on assignments and live projects, learners gain practical skills to work on real AI-driven applications and grow toward job-ready roles.
Students & College Freshers
Job Seekers & Career Switchers
Beginners Interested in AI & ML
Developers Exploring Generative AI
Curriculum
Complete Course Syllabus
01
Foundations of AI (The Basics)
Chapter 1: Introduction to AI
What is Artificial Intelligence? — plain-language definition and everyday analogies
AI vs Machine Learning vs Deep Learning — clear comparison
Industry impact of AI in healthcare, finance, and other domains
Rule-based systems vs learning-based systems
AI limitations including hallucinations and bias
Practical examples such as recommendation systems, maps, grammar tools, and face unlock
Chapter 2: History & Evolution of AI
Alan Turing and the Turing Test
AI Winters and the ups and downs of AI progress
Dartmouth workshop, Deep Blue, and major AI milestones
Deep Learning revolution and ImageNet breakthrough
Evolution from GPT-1 to GPT-4 and modern AI systems
Predicting the next waves such as multimodal AI and AGI
Chapter 3: Data Literacy
What data is and why it powers AI systems
Structured vs unstructured data
Data types such as numerical and categorical data
The data pipeline and common data quality issues
Introduction to Pandas and working with datasets
Data governance, privacy, feature engineering, and avoiding data leakage
Chapter 4: Logic & Mathematics for AI
AI as applied statistics and probability
Statistics basics including mean, variance, and simple interpretation
Linear algebra concepts such as vectors and matrices
Calculus intuition and gradient descent
Probability distributions and loss functions
Overfitting, bias-variance trade-off, and selecting suitable loss functions
02
Machine Learning Fundamentals (The Engine)
Chapter 1: Supervised Learning
Concept of labelled data and supervised learning
Regression vs classification
Linear regression and logistic regression
Training loop and data splitting strategies
Regularisation techniques such as Lasso and Ridge
Handling class imbalance and avoiding data leakage between train and test sets
Chapter 2: Unsupervised Learning
Learning without labels and pattern discovery
Clustering with K-Means
Hierarchical clustering and dendrogram basics
Principal Component Analysis (PCA)
Association rules and Apriori algorithm
Anomaly detection and curse of dimensionality concepts
Chapter 3: Key ML Algorithms
Decision Trees and K-Nearest Neighbors (KNN)
Polynomial regression and tree-based models
Random Forests and ensemble techniques
Support Vector Machines (SVM)
Selecting the right algorithm for the problem
Hyperparameter tuning with approaches like GridSearchCV
Chapter 4: Model Evaluation
Why accuracy alone can be misleading
Confusion matrix, precision, recall, and F1-score
ROC-AUC and classification performance analysis
Cross-validation and K-Fold validation
Regression metrics such as MAE, RMSE, and R²
Monitoring model drift and production-level evaluation basics
03
Deep Learning & Neural Networks (Mimicking the Brain)
Chapter 1: Neural Network Architecture
Biological neuron analogy and how neural networks are inspired by the brain
Input, hidden, and output layers
Weights, biases, and activation functions like Sigmoid, ReLU, and Softmax
Backpropagation, epochs, batch size, and learning rate
Keras basics for building neural network models
Vanishing gradients, dropout, and transfer learning basics
Chapter 2: Computer Vision (CNNs)
Understanding digital images as pixels
Filters, feature maps, and convolution basics
CNN layers including convolution, pooling, and dense layers
Data augmentation and image preprocessing
YOLO vs R-CNN concepts
Model interpretability, TFLite deployment, and distribution shift awareness
Chapter 3: Natural Language Processing (NLP)
Converting text into numbers for machine understanding
Tokenisation and word embeddings such as Word2Vec and GloVe
Bag of Words and TF-IDF
RNNs, LSTMs, and transformer architecture
Fine-tuning BERT and common NLP metrics like BLEU and ROUGE
Hallucination reduction with RAG and sentiment analysis applications
04
Generative AI & Prompt Engineering (The Modern Era)
Chapter 1: Large Language Models (LLMs)
What LLMs are and how they generate intelligent responses
Transformer architecture and context windows
Pre-training vs fine-tuning
Comparing GPT, Gemini, Claude, and Llama families
RLHF, RAG, and AI agents with tool use
LLM evaluation benchmarks and practical business use cases
Chapter 2: Masterclass in Prompt Engineering
Why prompt quality matters for AI output quality
Zero-shot vs few-shot prompting
Anatomy of a strong prompt: role, context, task, format, and constraints
Chain-of-thought, prompt chaining, and system prompts
Structured outputs in JSON and Markdown
Prompt injection, jailbreaking, and iterative prompt refinement
Chapter 3: AI Ethics & Safety
AI bias, discrimination, and fairness concerns
Deepfakes and misinformation risks
Privacy and responsible AI use
Governance frameworks and explainable AI concepts
Alignment problem and responsible AI checklists
Watermarking, C2PA, and methods for fighting harmful AI misuse
05
Capstone Projects & Career Path
Project 1: Personal AI Chatbot
Build a custom chatbot using OpenAI or Gemini APIs
Learn API integration and system prompt design
Create a frontend using Streamlit or Gradio
Stretch goals: RAG with Google Sheets and multilingual support
Project 2: Sentiment Analysis Tool
Classify emotions in product reviews, feedback, or tweets
Build an NLP pipeline for text analysis
Fine-tune DistilBERT and evaluate model performance
Stretch goals: aspect-based sentiment analysis and live dashboards
Project 3: Image Classifier
Build an image classifier for object categories
Learn custom dataset collection and preprocessing
Train CNN models with Keras and use transfer learning with MobileNetV2
Stretch goals: web app deployment and Grad-CAM visualisation
Career Guide: Building Your AI Portfolio & Finding Roles
Portfolio essentials: GitHub, demos, personal branding, and project presentation
LinkedIn profile building for AI-focused opportunities
High-demand roles: ML Engineer, AI Engineer, Prompt Engineer, Data Scientist, and AI Product roles
Recommended certifications in Google, AWS, and DeepLearning.AI tracks
Final action plan for project completion, profile updates, and applying for jobs
Why Choose This Course
Course Benefits
Hands-On AI Projects
Work on real AI and machine learning projects that strengthen your portfolio.
Generative AI Skills
Learn prompt engineering, LLM workflows, and modern AI tools used in industry.
Career Guidance
Get help with resumes, LinkedIn, interview preparation, and AI career planning.
Small Batch Sizes
Max 15 students per batch for more interaction, mentorship, and doubt support.
Expert Mentorship
Learn from mentors with practical experience in AI, ML, automation, and software systems.
Post-Course Support
Continue getting guidance, doubt-solving, and project support after completion.
Certification
Earn a Certificate That Strengthens Your AI Career
After completing all modules and live projects, you'll receive an official Artificial Intelligence Course Completion Certificate from SukhNexus IT Solutions — a verified credential that adds value to your resume, LinkedIn, and professional profile.
Issued by SukhNexus with a unique certificate ID
Shareable on LinkedIn & Resume
Verifiable by employers online
Valuable for internships and job applications
Enroll & Get Certified
SukhNexus IT Solutions
Certificate of Completion
🤖
This certifies that
Your Name Here
Artificial Intelligence Course
has successfully completed all modules
Verified
Date: ___________ID: SN-AI-2024
Ready to Start Your AI Journey?
Join the next batch. Limited seats — enroll today and secure your place in the Artificial Intelligence Training Program.