Our company specializes in consulting, product development, and customer support. We tailor our services to fit the unique needs of businesses across various sectors, helping them grow and succeed in a competitive market.

Tools: Python, Git  

• Introduction to Git (Version Control Basics, Importance of Git) 

• Git Installation & Setup (Installing Git, Configuring User Information) 

• Git Fundamentals (Initializing a Repository, Cloning, Staging, Committing) 

• Branching & Merging (Creating Branches, Switching, Merging, Resolving Conflicts)

• Remote Repositories (Pushing, Pulling) 

• GitHub & Collaboration (Forking, Pull Requests, Code Reviews) 

• Git Workflow Strategies (Feature Branch, Gitflow, Trunk-based Development)

• Undoing Changes (Git Reset, Revert, Checkout, Amend)

• Git Ignore & Clean-Up (Ignoring Files, Removing Untracked Fi

Project: Collaborative Codebase Management with Git.

This project will involve setting up and managing a collaborative software development workflow using Git and GitHub. The goal is to simulate a real-world team development environment where multiple contributors work on different features, merge changes, resolve conflicts, and maintain version control best practices. Participants will explore Git commands, branching strategies, remote repository management, and GitHub collaboration features.

Tools: Python, Jupyter notebook, VS Code 

• Python Installation & Setup 

• Python Basics (Variables, Strings, Operators, Control Flow - If Condition, For & While Loop) 

• Data Structures (Lists, Tuples, Dictionaries, Sets) 

• Functions & Modules (Built-in, User-Defined functions & Libraries) 

• File Handling & Exception Handling (File Handling, Exception Handling)

• Working with NumPy (Introduction & Benefits, Basic Operations, Matrix Operations, Slicing & Stacking)

• Data Analysis with Pandas (Pandas Introduction and Installation) 

• Object-Oriented Programming (OOP) (Classes and Objects, Inheritance, Operator Overloading)

• Advanced Python Concepts (Recursion, Generators, Decorators, Lambda Functions) 

• Working with APIs & Web Scraping (Requests, Beautiful Soup) 

Project: Auto Mobile Sales Market Analysis.

This project will analyze automobile sales data to uncover market trends, customer preferences, and the impact of various factors like price, brand, and seasonality. You will explore data-driven insights for business decisions, covering Python programming, data analysis and visualization.

Tools: MySQL & Power BI

• Installing & Setting Up MySQL

• Basic SQL Queries (SELECT, WHERE, DISTINCT, LIKE, BETWEEN, IN, ORDER BY, LIMIT)

• Summary Analytics (MIN, MAX, AVG, GROUP BY, HAVING) 

• Joins & Subqueries (INNER, LEFT, RIGHT, FULL, CROSS, Correlated Subquery) 

• Common Table Expression (CTE, CTE Benefits & Other Applications) 

• Window Functions (OVER Clause, ROW_NUMBER, RANK, DENSE_RANK) 

• Database Design & Modelling (Primary Key, Foreign Key, Entity Relationship, Database Normalization)

• Data Types in SQL. 

• Data Manipulation (INSERT, UPDATE, DELETE)

• Data Warehousing & Business Intelligence (OLAP vs OLTP, Fact vs Dimension, Star vs Snowflake Schema)

Project: Sales Data Management & Analytics System.

This project will manage and analyze sales data to track revenue trends, customer purchasing behavior, and product performance. You will explore data-driven insights for business decisions, covering MySQL database management, query optimization, data analytics, and visualization using Power BI.

Tools: Python

• Types of Data & Statistical Analysis (Descriptive vs. Inferential Statistics, Univariate vs. Bivariate vs. Multivariate Analysis)

• Measures of Central Tendency & Dispersion (Mean, Median, Mode, Range, Variance, Standard Deviation, IQR)

• Outliers & Data Distribution (Box or Whisker Plot, Outlier Treatment Using IQR and Box Plot, Skewness, Confidence Interval)

• Probability & Rules (Probability Basics, Conditional Probability, Bayes Theorem)

• Statistical Distributions & Z Score (Normal Distribution, Detect Outliers Using Normal Distribution, Standard Normal Distribution (SND), Z Score)

• Sampling & Hypothesis Testing (Random Sampling & Sample Bias, Central Limit Theorem, Sampling Distribution, Standard Error)

• Hypothesis Testing & Significance (Null vs. Alternate Hypothesis, Z Test, Rejection Region, p-Value, Statistical Power & Effect Size)

• Statistical Tests (Z Test, T-test, Chi-squared Test of Independence)

• Data Visualization & Charts (Pie Chart, Bar Chart, Histograms, Line Chart, Scatter Plot, Bubble Plot)

Project: Customer Purchase Behavior Analysis.

This project will analyze customer purchasing behavior using statistical methods, probability, and hypothesis testing. You will explore data-driven insights to identify trends, detect anomalies, and make data-driven decisions, covering data analysis, visualization, and statistical testing using Python and SQL.

Tools: Python, Scikit-learn.

• Introduction to Machine Learning (Supervised vs Unsupervised Learning, Classification vs Regression)

• Regression Models (Simple Linear Regression, Multiple Linear Regression)

• Classification Models (Logistic Regression, Support Vector Machine (SVM), Naive Bayes, Decision Tree)

• Optimization & Cost Functions (Cost Function, Log Loss, Gradient Descent, Derivatives and Partial Derivatives, Chain Rule)

• Model Evaluation & Regularization (Bias-Variance Trade-Off, L1 and L2 Regularization, Overfitting and Underfitting)

• Data Preprocessing & Feature Engineering (One Hot Encoding, Scaling, Variance Inflation Factor (VIF), Handle Class Imbalance, Sklearn Pipeline)

• Ensemble Learning (Majority Voting, Average & Weighted Average, Bagging, Random Forest, Boosting - AdaBoost, Gradient Boosting, XGBoost)

• Model Evaluation & Validation (Accuracy, Precision, Recall, F1 Score, Confusion Matrix, ROC Curve & AUC, Stratified K Fold Cross Validation, K Fold Cross Validation)

• Hyperparameter Tuning (Model Fine-Tuning, Hyperparameter Optimization)

• Clustering Techniques (K-Means Clustering)

Project: Customer Churn Prediction.

This project will predict customer churn using machine learning techniques, covering data preprocessing, feature engineering, model training, and evaluation. You will explore classification models, ensemble learning, and hyperparameter tuning to improve predictive performance.

Tools: Python, Scikit-learn, TensorFlow 

• Introduction to Deep Learning (Deep Learning vs Statistical ML, Applications of Deep Learning)

• Neural Networks & Architectures (Neuron, Perceptron, MLP, Neural Network Architectures)

• Frameworks & Hardware (PyTorch vs TensorFlow, GPU, TPU)

• Model Training & Optimization (Training through Backpropagation, Gradient Descent, Batch GD vs Mini Batch GD vs SGD, Gradient Descent with Momentum, Adam Optimizer)

• Activation Functions (Sigmoid, ReLU, Tanh, SoftMax)

• Regularization & Hyperparameter Tuning (Dropout Regularization, Batch Normalization, Hyperparameter Tuning, Optuna)

• Convolutional Neural Networks (CNN) (Model Training with CNN)

• Recurrent Neural Networks (RNN, LSTM) 

• Transformer Models & NLP (Transformer Architecture, Word Embeddings, Attention Mechanism, Hugging Face - BERT Basics)

• Challenges in Deep Learning (Vanishing Gradient Problem)

• Model Deployment (Model Training using Transfer Learning, Streamlit App, FastAPI Server)

Project: Image Classification Using Deep Learning.

This project will build an image classification model using deep learning techniques, covering neural networks, convolutional architectures, transfer learning, and model optimization. You will implement the model in PyTorch, fine-tune hyperparameters, and deploy it using a web app.

Tools: Python, Scikit-learn, NLTK, Spacy, Hugging Face.

• Introduction to NLP (NLP Pipeline, Tools Overview)

• Text Preprocessing (Tokenization Techniques, Stemming & Lemmatization, Stop Words)

• Part of Speech & Named Entity Recognition (POS Tagging, Named Entity Recognition - NER)

• Feature Engineering in NLP (Bag of Words, n-grams, TF-IDF, Word Embeddings)

• Regular Expressions in NLP (Regex for Text Processing)

• Hugging Face for NLP (Introduction to Hugging Face, Pipelines and Tokenizers)

Project: News Classification Using NLP.

This project will build a news classification system using Natural Language Processing (NLP). You will preprocess text, extract features, and classify news articles into different categories using traditional and transformer based models. The project will cover the NLP pipeline, feature engineering, and deep learning techniques, leading to a deployable model.

Tools: Python, Open AI LLMs, DeepSeek. 

• Introduction to Generative AI (Overview, Applications)

• Large Language Models (LLMs, Context Window, Temperature, Top-p, Top-k)

• Prompt Engineering (Elements of a Good Prompt, Zero-Shot, One-Shot, Few-Shot Prompting)

• Generative AI Application Development (Development Steps, Hallucinations, Security, Cost)

• LangChain Framework (Installation, Calling LLM from LangChain, Prompt Templates & Chains)

• Retrieval-Augmented Generation (RAG) (Vector Database, ChromaDB, Metadata Filtering, Euclidean & Cosine Distance)

• Agents and Agentic AI (Understanding Agents in AI)

• Streamlit UI Development (Building UI for Generative AI Apps)

• Database Integration (SQLite for Storing Data in AI Applications)

Project : AI-Powered Knowledge Assistant.  

This project will develop an AI-powered knowledge assistant that retrieves relevant information from a custom database using Generative AI and Retrieval-Augmented Generation (RAG). The assistant will integrate with a vector database for efficient search, use LangChain for LLM interaction, and have a Streamlit-based user interface for seamless interaction.

Clinical Decision Support with RAG – Leveraging Retrieval-Augmented Generation to enhance medical decision-making.

Autonomous Retail Analytics – Predicting sales trends using a combination of CNN and ML models.

AI-Driven Medical Diagnosis – Employing deep learning for accurate disease detection.