AI & Machine Learning Course in Bhopal | Miracle IT Career Academy
ARTIFICIAL INTELLIGENCE & MACHINE LEARNING

AI & Machine Learning Course in Bhopal

Build, Train & Deploy Intelligent AI Systems

Master vectorized Python programming, supervised and unsupervised machine learning with Scikit-Learn, deep neural networks with PyTorch and TensorFlow, computer vision with OpenCV, and GenAI LLM APIs with production FastAPI endpoints.

Book Free Career Counselling Visit Explore Curriculum
4 - 6 Mo Verified Duration
100% Lab Practice
5+ Production ML Models
1 : 1 Daily Mentorship
⚡ 5 Industry-Standard AI / ML Stacks ● Click any card to spotlight
PYTHON & NUMPY
NumPy Logo

Python & NumPy

Vectorized Math

Linear Algebra • Arrays • Matrices

SCIKIT-LEARN
Scikit-Learn Logo

Scikit-Learn

Machine Learning

Regression • SVM • XGBoost Ensembles

PYTORCH
PyTorch Logo

PyTorch

Deep Learning & Tensors

Autograd • Neural Nets • Backprop

TENSORFLOW
TensorFlow Logo

TensorFlow

Deep Model Architecture

Keras • Convolutions • Checkpoints

OPENCV & GENAI
OpenCV Logo

Vision & GenAI

Vision & LLM Endpoints

Object Detection • FastAPI • LLM APIs

Personalized Learning Paths

Who Is This Course For?

Explore the right learning path based on your current background and machine learning career goals.

College Students

CS / IT / BCA Students

Overcome theoretical engineering syllabus. Train actual neural networks and build production-ready ML models on high-performance workstation GPUs.

  • Minor & Major Project Support
  • Flexible Morning & Evening Batches
  • Kaggle & Colab Model Training
Dev Career Shift

Software Developers

Supercharge your backend engineering experience with AI model pipelines, PyTorch inference servers, and scalable FastAPI microservices.

  • Production FastAPI Microservices
  • Docker ML Model Containerization
  • Real-Time Model Inference APIs
Job Seekers

Fresh Tech Graduates

Graduated recently and want high-growth AI roles? Build a verifiable GitHub portfolio of predictive algorithms and computer vision systems.

  • Technical Mock Interview Desk
  • 5+ Production AI/ML Capstones
  • Resume & GitHub Profile Reviews
Analytics Upgrade

Data Analysts & BI

Upgrade from retrospective charts to predictive intelligence. Learn gradient boosting, neural networks, and automated machine learning workflows.

  • Predictive Modeling Workflows
  • Scikit-Learn Feature Selection
  • Time-Series Forecasting
GenAI Builder

GenAI & Tech Enthusiasts

Move beyond casual prompt engineering. Learn how LLMs work internally, implement vector embeddings, build RAG pipelines, and integrate OpenAI APIs.

  • RAG Vector Databases
  • Computer Vision with OpenCV
  • 1:1 In-Person Senior Mentorship
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STRUCTURED LEARNING PATH

Structured 5-Stage Mastery Flow

A structured learning flow from vectorized Python mathematics to deep learning neural networks, computer vision, and production GenAI LLM serving.

Scroll to explore the 5 stages
01

Python Foundations & Linear Algebra

• WEEKS 1–3 — FOUNDATIONAL MATHEMATICS MILESTONE

Lay the mathematical and computational bedrock for machine learning. Master vectorized operations in NumPy, matrix multiplication, eigenvalues, gradient descent calculus, and algorithmic complexity.

NumPy Multi-Dimensional Arrays Vectorization & Broadcasting Matrix Math & Inversion Calculus & Gradient Descent Basics
Core Technologies:
Python Python 3
NumPy NumPy
Stage 1 Output: Vectorized Matrix Operations & Gradient Descent
Validated
>>> import numpy as np
>>> weights = np.random.randn(512, 128) * 0.01
>>> grad = np.dot(X_batch.T, errors) / len(X_batch)
[TENSOR] Forward pass latency: 1.2ms (Vectorized SIMD)
50x NumPy Speedup vs Loops
100% Pure Math Foundations
Stage 2 Output: Pandas Pipeline & Correlation Heatmap
Cleaned Dataset
>>> df_clean = raw_df.pipe(impute_missing).pipe(encode_categorical)
>>> corr_matrix = df_clean.corr(numeric_only=True)
[EDA] Skewness reduced: 3.42 -> 0.12 (Box-Cox Transform)
1.4M+ Records Cleaned
0% Missing Value Leakage
02

Exploratory Data Analysis & Feature Pipelines

• WEEKS 4–6 — FEATURE ENGINEERING MILESTONE

Clean messy real-world datasets with Pandas. Handle missing value imputation, detect distribution outliers, build one-hot encoding matrices, and transform raw variables into predictive features.

Pandas DataFrame Transformations Missing Value & Outlier Imputation One-Hot & Ordinal Encoding Correlation Analysis & Normalization
Core Technologies:
Pandas Pandas
NumPy NumPy
Python Seaborn / Matplotlib
03

Supervised, Unsupervised ML & Ensembles

• WEEKS 7–10 — MACHINE LEARNING MODELS MILESTONE

Train robust machine learning models using Scikit-Learn. Implement linear & logistic regression, decision trees, random forests, gradient boosting (XGBoost), k-means clustering, and cross-validation pipelines.

Scikit-Learn Pipelines Random Forest & XGBoost Classifiers K-Fold Cross-Validation Precision, Recall, ROC-AUC Metrics
Core Technologies:
Scikit-Learn Scikit-Learn
Python XGBoost
Stage 3 Output: XGBoost Cross-Validation & ROC Curve
94.8% ROC-AUC
>>> model = XGBClassifier(n_estimators=300, learning_rate=0.03)
>>> scores = cross_val_score(model, X_train, y_train, cv=5)
[EVAL] Mean 5-Fold F1-Score: 0.942 | ROC-AUC: 0.968
94.8% ROC-AUC Score
5-Fold Rigorous CV
Stage 4 Output: PyTorch CNN Training & OpenCV Vision
Epoch 45 / 50
>>> class ConvNet(nn.Module): def __init__(self)...
>>> loss.backward(); optimizer.step()
[TRAIN] Loss: 0.081 | Val Acc: 97.4% (CUDA Accelerated)
97.4% Validation Accuracy
45 FPS OpenCV Inference
04

PyTorch Neural Networks & Computer Vision

• WEEKS 11–15 — NEURAL NETWORKS & COMPUTER VISION MILESTONE

Construct and train multi-layer neural architectures with PyTorch and TensorFlow. Master backpropagation, convolutional neural networks (CNNs), transfer learning with ResNet, and real-time image processing with OpenCV.

PyTorch Tensors, Autograd & CUDA Convolutional Neural Networks (CNN) Transfer Learning (ResNet / EfficientNet) OpenCV Real-Time Object Detection
Core Technologies:
PyTorch PyTorch
TensorFlow TensorFlow
OpenCV OpenCV
05

Generative AI, RAG & Production Serving

• WEEKS 16–20 — GENAI & MLOPS PRODUCTION SERVING MILESTONE

Bridge models from Jupyter notebooks to production servers. Integrate LLM APIs with vector embeddings (RAG), containerize with Docker, and serve low-latency prediction endpoints using FastAPI.

LLM APIs & Prompt Engineering RAG Architecture & Vector DBs FastAPI Asynchronous Model Endpoints Docker Containerization & Deployment
Core Technologies:
FastAPI FastAPI
Docker Docker
Git Git / GitHub
Stage 5 Output: FastAPI Swagger Docs & Docker Inference
Production Ready
> docker run -p 8000:8000 --gpus all aiml-api:v2
> curl -X POST /predict -d '{"features": [...]}'
[FASTAPI] HTTP 200 OK | Latency: 14ms | GPU Util: 68%
14ms P95 Inference Time
Docker Containerized API
Free 1-on-1 Roadmap Consultation

Ready to Master All 5 AI & ML Stages?

Schedule a personalized counselling session at our M.P. Nagar campus. We'll map your mathematics and coding background directly to this AI/ML engineering syllabus.

Book Free Counselling Visit Call: +91 78800 03127
Applied AI Pedagogy

How You'll Learn

A transparent, practical learning journey designed to replace passive tutorials with hands-on model training, GPU lab experiments, and production deployment.

01
STEP 01 — INTUITION

Mathematical & Algorithmic Intuition

Concept-first sessions in smart classrooms where instructors demystify cost functions, gradient descent, decision boundary geometry, and neural activation dynamics.

  • ✓ Visual Gradient Descent Derivations
  • ✓ Matrix Transformation Geometry
  • ✓ Loss Function & Probability Mechanics
02
STEP 02 — WORKSTATION LAB

Practical Lab Experiments

Step directly into our dedicated workstation lab in M.P. Nagar. Clean datasets with Pandas, train models in Scikit-Learn, and run PyTorch deep learning training on your assigned PC.

  • ✓ Dedicated Workstation per Student
  • ✓ JupyterLab & VS Code Pre-Configured
  • ✓ GPU-Accelerated Model Training
03
STEP 03 — CAPSTONE REPOSITORY

Production Capstone Projects

Architect end-to-end machine learning systems. Train classification models, build computer vision inspection pipelines, and deploy FastAPI prediction endpoints on Docker.

  • ✓ Production Datasets & Real Workflows
  • ✓ FastAPI & Docker Deployment
  • ✓ Public GitHub Code Portfolio
04
STEP 04 — 1-ON-1 GUIDANCE

1-on-1 Mentor Support Desk

Never stay stuck on vanishing gradients, tensor shape mismatches, or Docker container errors. On-floor senior AI practitioners sit with you 1-on-1 to review code and debug models.

  • ✓ Daily 1-on-1 Mentor Desk in Lab
  • ✓ Hyperparameter & Loss Curve Tuning
  • ✓ GitHub & Technical Interview Reviews
Practical Competence

Build With Real Production AI Projects

Proof of practical competence. Train, evaluate, and deploy production machine learning algorithms on your verified GitHub portfolio.

Healthcare Machine Learning Diagnosis Model at Miracle IT
Live ML Model Scikit-Learn + XGBoost

PredicTx — Clinical Healthcare Diagnostic System

Predictive risk scoring engine evaluating multi-variate patient diagnostic records, handling high-cardinality biomedical features, and explaining risk decisions with SHAP values.

What the learner practices:

Missing value imputation pipelines, SMOTE class balancing, hyperparameter cross-validation, and explainable AI (XAI) feature attribution.

Scikit-Learn XGBoost Pandas SHAP FastAPI
Real-Time Computer Vision and Defect Detection in Bhopal Lab
Deep Learning Vision PyTorch + OpenCV

VisionGuard — Real-Time Industrial Defect Detection

High-throughput automated quality control system inspecting high-speed camera streams, classifying surface anomalies, and localizing micro-cracks with bounding boxes in <30ms.

What the learner practices:

PyTorch transfer learning with ResNet, image augmentation with Albumentations, bounding box IoU calculation, and real-time OpenCV video processing.

PyTorch OpenCV CNN ResNet CUDA
GenAI RAG Knowledge Assistant at Miracle IT
GenAI RAG Pipeline LLM APIs + Vector DB

DocuMind — Enterprise RAG Knowledge Assistant

Full-stack Retrieval-Augmented Generation assistant indexing thousands of PDF technical manuals, executing vector semantic search, and streaming responses with source citations.

What the learner practices:

Document chunking strategies, dense vector embeddings, ChromaDB / FAISS vector indexing, guardrailed system prompting, and containerized FastAPI serving.

FastAPI Docker Vector DB LLM APIs Python
Review Student Code Repositories In-Person
Structured Transition

Career Support

Comprehensive preparation and guidance to help you transition from practical learner to industry-ready AI & Machine Learning engineer.

Career Guidance & Counselling

Personalized skill audits and roadmaps for targeted roles (ML Engineer, Computer Vision Developer, Data Scientist, GenAI Engineer).

1-on-1 Mentorship Desks

Resume & GitHub Portfolio

Assistance in structuring an employer-ready machine learning resume, showcasing public GitHub model repos, Kaggle benchmarks, and Docker endpoints.

Portfolio Formatting

Interview Preparation

Master standard ML engineering interviews: gradient descent calculus, bias-variance tradeoff, regularization, CNN backprop, and metrics evaluation.

Technical Question Banks

Internship Support

Guidance on securing junior ML internships, applied AI research trainee positions, and computer vision project roles across tech firms.

Internship Roadmap

Placement Support

Connecting verified students with scheduled hiring drives, off-campus recruitment opportunities, and partner tech company referrals.

Hiring Drive Alerts

Mock Technical Rounds

Simulated technical interviews conducted by senior data science and AI practitioners to evaluate live coding, model explanations, and ML system design.

Detailed Feedback Reports
Honest Academic Policy: We provide structured internship support, placement assistance, resume reviews, and technical mock interviews. We uphold complete transparency and do not make unsubstantiated claims regarding guaranteed jobs, guaranteed placements, or guaranteed salaries.
Talk To Counsellor
Complete Transparency

Course Details

Verified course specifications, batch flexibility, and transparent fee policies. No ambiguous promises.

Duration
4 - 6 Months

4 Months: Core Machine Learning (Python, NumPy, Pandas, Scikit-Learn, XGBoost).
6 Months: Full AI Track including PyTorch, Computer Vision (OpenCV), and GenAI LLM FastAPI deployments.

Eligibility
Open to All Streams

B.Tech, BCA, MCA, B.Sc (Maths/Stats/CS/IT), Diploma, or graduates with analytical interest. Mathematical concepts and Python coding taught from ground zero.

Batch Mode & Timings
In-Person & Flexible

Mode: In-person workstation GPU lab training in M.P. Nagar.
Slots: Morning, Afternoon, Evening, and Weekend Special Batches for college students and working professionals.

Fees
Affordable & EMI

Transparent fee structure with easy monthly installment (EMI) options. Inclusive of all GPU workstation lab access, capstone project reviews, and placement mock rounds. Zero hidden charges.

Want exact fee details and available scholarship concessions?

We believe in 100% upfront honesty. Visit our M.P. Nagar center to inspect the syllabus, speak directly with AI faculty, and receive a transparent, printed fee roadmap.

Schedule Free Fee & Syllabus Audit
Verified Student Proof

Student Experience

Real results and verified reviews from students trained in our Bhopal workstation GPU labs.

Astik Sarathe placed at Infosys Astik Sarathe Infosys
Akshat placed at Accenture Akshat Accenture
Anuj Kumar Kaushal placed at TCS Anuj Kaushal TCS
Siddhant placed at Infosys Siddhant Infosys
Namit Shirode placed at Uline USA Namit Shirode Uline (USA)
Mohit placed at Dot9 Games Mohit Dot9 Games

"Training PyTorch neural networks and tuning hyperparameters directly on the academy's lab workstations gave me an incredible advantage. We didn't just write theory — we trained actual computer vision and predictive models."

Ankita

Ankita

ML Trainee • Miracle IT Alumna

"The practical depth in Scikit-Learn pipelines and XGBoost was amazing. Building the healthcare diagnostic capstone and writing custom data transforms gave me the confidence to ace technical interviews."

Vinod

Vinod

Junior ML Engineer • Miracle IT Graduate

"My overall experience has been fantastic. Whenever my loss function diverged or tensor shapes mismatched, the mentors sat with me 1-on-1 until everything clicked."

Nilu

Nilu

AI & Data Science Student • Miracle IT

"It's one of the few institutes in MP that actually teaches deploying machine learning with FastAPI and Docker. You walk away with a live GitHub portfolio that hiring managers respect."

Mohit

Mohit

AI Software Engineer • Miracle IT Alumnus
See Student Experience
Common Enquiries

Frequently
Asked Questions

Straightforward answers about our AI & Machine Learning course, GPU workstation lab, math foundations, and free 1-on-1 career counselling in M.P. Nagar, Bhopal.

Have a different question?

Talk to a Counsellor

No! The course starts with clean Python fundamentals, progressive NumPy vectorized array manipulation, and intuitive linear algebra. You do not need to memorize abstract formulas before joining — we teach mathematical intuition visually alongside code implementations.

You will master: Python 3, NumPy, Pandas, Matplotlib/Seaborn, Scikit-Learn, XGBoost, PyTorch (Tensors, Autograd, CNNs), TensorFlow / Keras, OpenCV for computer vision, FastAPI for prediction REST endpoints, Docker for model containerization, and OpenAI LLM APIs with vector databases (ChromaDB / FAISS).

Yes! Every enrolled student is assigned a dedicated workstation in our air-conditioned M.P. Nagar lab. Systems are equipped with pre-configured CUDA environments, JupyterLab, and VS Code, ensuring you can train neural networks and run OpenCV video processing smoothly.

You will construct 3 verified production capstones: (1) PredicTx — Clinical Healthcare Diagnostic ML System using Scikit-Learn & SHAP values, (2) VisionGuard — Real-Time Industrial Defect Detection using PyTorch CNNs & OpenCV, and (3) DocuMind — Enterprise RAG Knowledge Assistant serving LLM endpoints via FastAPI and Docker.

Graduates are qualified for roles including Junior Machine Learning Engineer, Applied AI Developer, Data Scientist (Associate), Computer Vision Engineer, GenAI Integration Specialist, and Python Data Engineer across tech startups, IT firms, and enterprise innovation centers.

Yes! We encourage every prospective student to book a free 1-on-1 counselling visit. You can walk through our workstation lab, test sample models on our machines, review past student capstone GitHub repos, and receive a customized career roadmap from our senior faculty.

In-Person Campus

Visit Us in M.P. Nagar, Bhopal

Centrally located in Zone-II, M.P. Nagar — Bhopal's primary educational hub. Accessible from all major transit links.

📍 Zone-2, M.P. Nagar Hub 🚆 5 Min from Rani Kamlapati (RKMP) 🚌 2 Min from Chetak Bridge / Sargam 🅿️ Dedicated Student Parking
In-Person Training Center
Visiting Desk Open Today

Miracle IT Career Academy

Campus Address:

Plot No.80, 3rd Floor, Aakriti Complex, Zone-2, M.P.Nagar, Bhopal, M.P.

Key Landmarks & Proximity:

Near Sargam Cinema & Chetak Bridge • Aakriti Complex Zone-2

Counselling & Visiting Hours:

Monday to Saturday: 10:00 AM – 7:00 PM (Sunday by appointment)

+91 78800 03127 WhatsApp Location Get Directions
M.P. Nagar Zone-II Coaching Hub

Zone-II, M.P. Nagar, Bhopal • Plot No.80, 3rd Floor, Aakriti Complex (Near Chetak Bridge & Sargam Cinema)

Free 1-on-1 AI & Machine Learning Counselling Desk

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