Job-Oriented AI ML Course in Nagpur

AI ML Course in Nagpur

Join Aspire Computer Institute's job-oriented Artificial Intelligence and Machine Learning course in Nagpur. Learn Python, SQL, Data Analysis, ML Algorithms, Deep Learning, NLP, Computer Vision, Generative AI, MLOps, Cloud Deployment, Live Projects, Resume Building and Interview Preparation.

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6-9 Mo

Complete AI ML training in Nagpur

15+

Live projects for AI ML portfolio

Cert

Course completion certification

Prep

Resume, GitHub & interviews

Course Overview

What You Will Learn

This AI ML course in Nagpur is designed to prepare students for real industry roles with strong programming, data handling, model building, deployment and project experience.

Python & Data Foundation

  • Python programming from basic to advanced
  • NumPy, Pandas and data analysis
  • Data visualization and EDA
  • SQL for data extraction

Machine Learning

  • Regression and classification algorithms
  • Clustering and recommendation systems
  • Feature engineering and model tuning
  • Real-world ML project workflow

Deep Learning

  • Neural networks and backpropagation
  • TensorFlow, Keras and PyTorch basics
  • CNN for image classification
  • LSTM and sequence modeling basics

NLP & Computer Vision

  • Text preprocessing and sentiment analysis
  • Transformers and Hugging Face
  • OpenCV image processing
  • Object detection and face recognition basics

Generative AI

  • Prompt engineering and LLM concepts
  • LangChain, LlamaIndex and RAG
  • Vector databases and embeddings
  • PDF chatbot and AI assistant projects

MLOps & Deployment

  • Flask, FastAPI, Streamlit and Gradio
  • Git, GitHub, Docker and MLflow
  • Model deployment and API testing
  • Cloud basics and final capstone project
Detailed Curriculum

AI ML Course Syllabus in Nagpur

Open each module to view the complete topic-wise syllabus designed for practical AI, Machine Learning, Generative AI and data science career preparation in Nagpur.

  • Python installation, VS Code, Jupyter Notebook and Google Colab
  • Variables, data types, operators and input/output
  • Conditional statements and loops
  • Functions, lambda functions and modules
  • Exception handling and file handling
  • List, tuple, set and dictionary
  • String operations and comprehensions
  • Object-oriented programming
  • Classes, objects, inheritance and polymorphism
  • Real-world Python practice programs

  • Linear algebra: vectors, matrices and dot product
  • Matrix multiplication, transpose and inverse
  • Calculus: derivatives, partial derivatives and gradients
  • Gradient descent and optimization basics
  • Probability and conditional probability
  • Bayes theorem and distributions
  • Mean, median, mode and standard deviation
  • Correlation and covariance
  • Hypothesis testing, p-value and confidence interval
  • Z-test, T-test, Chi-square and ANOVA basics

  • NumPy arrays, indexing, slicing and reshaping
  • Mathematical operations and broadcasting
  • Pandas Series and DataFrame
  • Reading CSV, Excel, JSON and database files
  • Filtering, sorting, grouping and merging data
  • Handling missing values and duplicates
  • Pivot tables and data transformation
  • Matplotlib and Seaborn visualization
  • Bar chart, line chart, histogram and scatter plot
  • EDA and business data analysis reports

  • Database concepts, tables, rows and columns
  • SELECT, WHERE, ORDER BY, GROUP BY and HAVING
  • Primary key and foreign key
  • INNER, LEFT, RIGHT, FULL and SELF JOIN
  • Aggregate functions and subqueries
  • Views and stored procedure basics
  • Window functions and CTE
  • Ranking functions
  • Data extraction for ML projects
  • Sales, customer and employee data analysis queries

  • Missing value handling and duplicate removal
  • Outlier detection and treatment
  • Data type conversion and text cleaning
  • Date-time feature extraction
  • Label encoding and one-hot encoding
  • Feature scaling and normalization
  • Feature selection techniques
  • PCA basics
  • Handling imbalanced datasets and SMOTE
  • Scikit-learn pipeline creation

  • AI vs ML vs Deep Learning
  • Supervised, unsupervised and reinforcement learning
  • Linear, multiple and polynomial regression
  • Ridge, Lasso and ElasticNet regression
  • Logistic Regression, KNN and Naive Bayes
  • SVM, Decision Tree and Random Forest
  • Gradient Boosting, XGBoost, LightGBM and CatBoost basics
  • K-Means, Hierarchical clustering and DBSCAN
  • PCA, t-SNE and anomaly detection
  • Recommendation system basics

  • Train-test split and cross-validation
  • Bias, variance, overfitting and underfitting
  • MAE, MSE, RMSE and R² score
  • Confusion matrix, accuracy, precision and recall
  • F1-score, ROC curve and AUC score
  • GridSearchCV and RandomizedSearchCV
  • Regularization techniques
  • Bagging, boosting and stacking
  • Voting classifier and ensemble methods
  • Model comparison and final model selection

  • Artificial neuron and perceptron
  • Activation functions and loss functions
  • Forward propagation and backpropagation
  • Gradient descent, SGD, RMSProp and Adam
  • ANN architecture for classification and regression
  • Dropout and batch normalization
  • TensorFlow, Keras and PyTorch basics
  • GPU training using Google Colab
  • Learning rate scheduling
  • Deep learning practical projects

  • Image reading, resizing, cropping and color conversion
  • Image filtering, thresholding and edge detection
  • Contours and face detection
  • Convolution, filters, pooling and flattening
  • CNN architecture and image classification
  • Transfer learning
  • Object detection and YOLO introduction
  • Image segmentation basics
  • Face recognition and OCR basics
  • Number plate, attendance and defect detection projects

  • Text preprocessing and tokenization
  • Stopword removal, stemming and lemmatization
  • POS tagging and named entity recognition
  • Bag of Words and TF-IDF
  • Sentiment analysis and spam detection
  • Word2Vec, GloVe and FastText
  • Attention mechanism and transformers
  • BERT and GPT basics
  • Hugging Face Transformers
  • Resume screening, chatbot and fake news detection projects

  • Generative AI and Large Language Models
  • Tokens, embeddings and context window
  • Prompt engineering: zero-shot and few-shot
  • Prompt templates and optimization
  • OpenAI, Gemini and Hugging Face model basics
  • LangChain and LlamaIndex
  • Vector databases: FAISS, ChromaDB and Pinecone basics
  • RAG pipeline: loader, splitter, embeddings and retrieval
  • PDF chatbot and website chatbot
  • AI resume analyzer and customer support chatbot projects

  • Time series trend, seasonality and noise
  • Moving average, stationarity and autocorrelation
  • ARIMA, SARIMA, Prophet and LSTM basics
  • Sales forecasting and inventory forecasting
  • Content-based and collaborative filtering
  • Movie, course, product and job recommendation systems
  • Reinforcement learning concepts
  • Agent, environment, state, action and reward
  • Q-learning basics
  • Exploration vs exploitation

  • Saving models using Pickle and Joblib
  • Flask and FastAPI model API
  • Streamlit and Gradio ML apps
  • REST API and Postman testing
  • Git and GitHub project management
  • Docker basics for ML deployment
  • MLflow experiment tracking and model registry
  • DVC and data versioning basics
  • CI/CD for ML basics
  • AWS, Azure and Google Cloud ML overview

  • Bias, fairness and explainability
  • Privacy, data security and AI misuse
  • SHAP, LIME and feature importance
  • GitHub portfolio setup
  • README writing and project documentation
  • ATS-friendly AI/ML resume
  • LinkedIn profile optimization
  • Python, SQL, statistics and ML interview questions
  • Project explanation practice
  • Mock interview and final presentation
Nagpur Location SEO

AI & Machine Learning Training in Nagpur

Aspire Computer Institute provides practical AI ML training for students, graduates and working professionals in Nagpur with project-based learning and career guidance.

Why Choose Aspire Computer Institute for AI ML Course in Nagpur?

Our AI ML course is focused on practical learning, live projects, resume preparation, GitHub portfolio building and interview preparation. Students learn from fundamentals to advanced industry topics such as Machine Learning, Deep Learning, NLP, Computer Vision, Generative AI, RAG and MLOps.

This course is suitable for students searching for AI ML course in Nagpur, Machine Learning classes in Nagpur, Artificial Intelligence training in Nagpur, Python ML course in Nagpur and Generative AI course in Nagpur.

Nagpur AI Training ML Course Generative AI Live Projects Job-Oriented

Course Highlights

  • Offline and online training support
  • Python, SQL, ML, DL, NLP, CV and GenAI
  • Real-world projects and capstone project
  • Resume, GitHub and interview preparation
  • Course completion certificate
  • Contact: +91 9175637604
Practical Learning

Real-World Projects Included

Students build portfolio-ready projects that can be added to GitHub, resume and LinkedIn profile.

Beginner ML

House Price Prediction

Build a regression model to predict property prices using real-world features and evaluation metrics.

Classification

Customer Churn Prediction

Predict whether a customer will leave a service using classification algorithms and feature engineering.

Finance AI

Credit Card Fraud Detection

Detect suspicious transactions using anomaly detection and imbalanced data handling techniques.

NLP

Resume Screening System

Create an AI system that analyzes resumes and ranks candidates based on job requirements.

Computer Vision

Face Recognition Attendance

Build an attendance system using OpenCV and face recognition concepts.

GenAI

PDF Question Answering Chatbot

Create a RAG-based chatbot that answers questions from uploaded PDF documents.

Forecasting

Sales Forecasting System

Forecast future sales using time series techniques and business data analysis.

Recommendation

Course Recommendation System

Recommend courses to students using content-based and collaborative filtering methods.

MLOps

End-to-End ML Deployment

Train, track, package and deploy an ML model using FastAPI, Docker, MLflow and GitHub.

Learning Path

Suggested Course Structure

A practical phase-wise roadmap to help students learn step-by-step and become job-ready.

Phase 1
Python, SQL, Mathematics & Statistics

Build strong programming and analytical foundation for AI/ML learning.

Phase 2
Data Analysis, Visualization & EDA

Learn how to clean, analyze and visualize real-world datasets.

Phase 3
Machine Learning Algorithms

Train regression, classification, clustering and recommendation models.

Phase 4
Deep Learning, NLP & Computer Vision

Work with neural networks, image processing, text data and transformers.

Phase 5
Generative AI, LLM & RAG

Build modern AI chatbots and document-based question-answering systems.

Phase 6
Deployment, MLOps & Interview Preparation

Deploy projects, prepare resume, build GitHub portfolio and practice interviews.

Career Opportunities

Job Roles After Completing This Course

This training prepares students for multiple AI, ML, data and automation-related job roles.

AI Engineer

Build AI-based solutions and intelligent automation systems.

ML Engineer

Train, optimize and deploy machine learning models.

Data Scientist

Analyze data, build models and generate business insights.

NLP Engineer

Work on chatbots, text analytics and transformer models.

CV Engineer

Build image classification, OCR and object detection apps.

GenAI Engineer

Create LLM apps, RAG systems and AI assistants.

MLOps Engineer

Manage ML lifecycle, model tracking and deployment.

AI Analyst

Apply AI/ML skills for business reporting and decisions.

FAQs

AI ML Course in Nagpur - Frequently Asked Questions

Common questions students ask before joining Artificial Intelligence and Machine Learning training at Aspire Computer Institute.

Who can join this AI ML course?

Students, graduates, freshers and working professionals from Nagpur who want to build a career in Artificial Intelligence, Machine Learning, Data Science or Generative AI can join this course.

Do I need programming knowledge?

Basic computer knowledge is enough to start. The course begins with Python programming, SQL, mathematics and statistics before moving to advanced AI and ML topics.

Will I work on live projects?

Yes. Students work on ML, NLP, Computer Vision, Generative AI, chatbot, forecasting and deployment projects that can be added to resume and GitHub portfolio.

Is this course job-oriented?

Yes. The syllabus includes job-ready skills, real projects, model deployment, MLOps basics, resume building, LinkedIn profile improvement and interview preparation.

Which tools are covered?

Python, SQL, NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, OpenCV, Hugging Face, LangChain, FastAPI, Streamlit, Docker, GitHub and MLflow are covered.

How can I contact for admission?

You can call or WhatsApp Aspire Computer Institute at +91 9175637604 for AI ML course admission details in Nagpur.

Join AI ML Course in Nagpur

Join Aspire Computer Institute in Nagpur and learn Artificial Intelligence, Machine Learning, Generative AI and MLOps with live projects, practical training, certification and career guidance.

Call: +91 9175637604 Chat on WhatsApp
© 2026 Aspire Computer Institute, Nagpur. AI ML Course in Nagpur - Job-Oriented Course Syllabus.