Swyam Prabha IT Academy Pvt. Ltd. / AI / ML
AI / MACHINE LEARNING

AI and Machine Learning Course in Bhopal

Learn machine learning, deep learning, neural networks and applied AI with Python through practical training, real projects and placement-focused preparation.

Start Your Journey → Explore Curriculum
100% Practical Learning
6–8 Months Program
6+ Major Projects
train.py

import tensorflow as tf

model = tf.keras.Sequential([

layers.Dense(128, activation="relu"),

layers.Dense(10, activation="softmax"),

])

model.fit(x_train, y_train, epochs=10)

Excel ML
Excel TensorFlow
Excel Python
01 Duration

6–8 Months

02 Mode

Classroom + Practical

03 Projects

6+ Real Projects

04 Career

Placement Preparation

COURSE DNA

Introduction to Artificial Intelligence and Machine Learning

One structured path from Python and machine learning to deep learning and deployment.

What Is Artificial Intelligence?

Artificial intelligence is the field of building systems that perform tasks that normally need human intelligence, such as recognising images, understanding language and making predictions.

Understanding Machine Learning Fundamentals

Machine learning is a subset of AI where systems learn patterns from data instead of being explicitly programmed. It is the foundation of most modern AI applications.

Python

Build strong programming fundamentals for AI work.

Math for ML

Linear algebra, calculus and probability essentials.

Machine Learning

Build and evaluate classical ML models.

Deep Learning

Train neural networks with modern frameworks.

Neural Networks

Understand how networks learn end to end.

NLP Basics

Work with text data and language models.

Computer Vision

Build image classification models with CNNs.

Model Deployment

Serve trained models behind a simple API.

LEARNING JOURNEY
From your first model to applied AI projects.

Learn in the order the industry actually needs you to learn.

01
FOUNDATION
Python Foundations

Build the Python skills needed for AI and ML work.

Core Python NumPy Pandas Environments
02
MATH
Math for ML

Understand the maths that powers machine learning.

Linear Algebra Calculus Basics Probability Gradients Optimisation
03
ML
Classical Machine Learning

Build supervised and unsupervised models and evaluate them.

Regression Classification Clustering Ensembles Evaluation
04
DEEP LEARNING
Deep Learning Foundations

Train neural networks with TensorFlow / Keras.

Perceptron Activation Functions Backprop Loss Functions Regularisation
05
PRACTICE
Neural Networks in Practice

Design, train and debug real networks.

Keras Layers Callbacks Overfitting Transfer Learning
06
APPLIED
Applied AI: NLP & Vision

Apply deep learning to text and images.

Text Preprocessing Embeddings CNNs Image Classification Pretrained Models
07
CAREER
Capstone & Interview Preparation

Prepare for AI / ML interviews and project discussions.

ML Concepts DL Concepts Case Studies Resume Mock Interviews
COMPLETE CURRICULUM

AI and Machine Learning Course Curriculum

Python Programming for AI and ML

Python, NumPy and Pandas for preparing data and building models.

Statistics and Data Preprocessing

The math essentials for ML — statistics, probability and linear algebra basics — plus cleaning, encoding and scaling data.

Supervised and Unsupervised Learning

Regression, classification, clustering and dimensionality reduction with scikit-learn.

Model Training and Evaluation

Train-test splits, cross-validation, metrics and tuning to build models you can trust.

Introduction to Deep Learning

Neural networks with TensorFlow, plus applied NLP and computer vision basics.

MODULE 01 01
Python for AI

Build the programming base for everything that follows.

  • Variables & Types
  • Control Flow
  • Functions
  • OOP Basics
  • NumPy
  • Virtual Envs
MODULE 02 02
Math Essentials

Learn just enough maths, applied to ML.

  • Vectors & Matrices
  • Matrix Operations
  • Derivatives
  • Gradients
  • Probability
  • Distributions
MODULE 03 03
Data Handling & EDA

Prepare data for modelling.

  • Pandas
  • Cleaning
  • Feature Engineering
  • Scaling
  • Train / Test Split
  • Visualization
MODULE 04 04
Supervised & Unsupervised ML

Build the core family of ML models.

  • Linear & Logistic Regression
  • Decision Trees
  • Random Forest
  • SVM
  • K-Means
  • PCA
MODULE 05 05
Model Evaluation

Make models reliable and honest.

  • Metrics
  • Confusion Matrix
  • Cross Validation
  • Bias / Variance
  • Hyperparameter Tuning
  • Data Leakage
MODULE 06 06
Deep Learning Foundations

Understand how neural networks learn.

  • Neurons & Layers
  • Activation Functions
  • Forward Pass
  • Backpropagation
  • Optimisers
  • Loss Functions
MODULE 07 07
CNNs & Computer Vision

Build models that understand images.

  • Convolutions
  • Pooling
  • CNN Architectures
  • Data Augmentation
  • Transfer Learning
  • Image Classification
MODULE 08 08
NLP Fundamentals

Build models that work with language.

  • Text Cleaning
  • Tokenisation
  • Bag of Words / TF-IDF
  • Word Embeddings
  • RNN Basics
  • Sentiment Analysis
MODULE 09 09
MLOps & Deployment Basics

Get a model out of the notebook.

  • Saving Models
  • REST API with Flask
  • Inference
  • Versioning
  • Monitoring Basics
  • Reproducibility
MODULE 10 10
Capstone & Placement

Deliver an applied AI project and prepare for interviews.

  • End-to-End Project
  • Presentation
  • ML / DL Interview Questions
  • Resume
  • Portfolio
  • Mock Interviews
PROJECT BASED LEARNING

Practical AI and Machine Learning Projects

Your portfolio should prove what you can build, not just what you have studied.

Build and Evaluate Machine Learning Models

Build a prediction model, an image classifier and a sentiment analysis app, then complete an applied AI capstone with a properly evaluated model.

  1. Machine learning

    Prediction Model

    Build and evaluate a classical ML model on a real tabular dataset.

    • scikit-learn
    • Evaluation
    • Tuning

  2. Computer vision

    Image Classifier

    Train a CNN to classify images and improve it with transfer learning.

    • Keras
    • CNN
    • Transfer Learning

  3. NLP

    Sentiment Analysis App

    Build a text-classification model and expose it through a simple interface.

    • NLP
    • Embeddings
    • Flask

Capstone project

Applied AI Project

Take a real problem from data and modelling through deep learning, evaluation and a deployed prediction API.

What you build it with
  • Python
  • TensorFlow
  • Evaluation
  • Flask API
  • GitHub
CAREER PREPARATION

Career Opportunities in AI and Machine Learning

Technical skills are only one part of becoming job-ready. We prepare you for the complete hiring process.

Machine Learning Engineer and AI Developer Roles

Prepare for roles such as Machine Learning Engineer, AI Developer, AI Engineer, NLP Engineer and Computer Vision Engineer.

Resume Building

Create an AI / ML-focused resume.

Project Portfolio

Showcase model notebooks and applied projects.

Technical Interviews

ML, deep learning, Python and maths questions.

Case Study Practice

Practice framing open-ended AI problems.

Mock Interviews

Experience real interview-style sessions.

YOUR CAREER JOURNEY 01 → 05
01 Learn
02 Practice
03 Build Projects
04 Build Portfolio
05 Get hired
CERTIFICATION
Complete the bootcamp. Build. Submit. Get certified.

Participants of our AI / ML Bootcamp receive a certificate after successfully completing and submitting the assigned AI / ML project.

01 AI / ML Bootcamp
02 Successful Project Submission
03 AI / ML Certificate
Submit Your Project →
Swyam Prabha IT Academy Pvt. Ltd. Swyam Prabha IT Academy Pvt. Ltd.
CERTIFICATE OF PARTICIPATION & PROJECT COMPLETION

This certificate is proudly presented to

Student Name

for successfully participating in the

AI / ML Bootcamp

and successfully completing and submitting the assigned AI / ML Project.

Swyam Prabha IT Academy certificate seal
Swyam Prabha IT Academy Pvt. Ltd. Certificate ID: SPAIML-2026
ADMISSION
Ready to start your AI / ML journey?

Talk to our counsellor and understand the batch, curriculum, fees and career path.

01
Enquire

Share your basic details with us.

02
Counselling

Discuss your goals with our counsellor.

03
Choose Your Batch

Select the schedule that works for you.

04
Start Learning

Begin your AI / ML journey.

Get Course Details

Our team will contact you shortly.

FAQ

Frequently Asked Questions About AI and ML Courses

What is the difference between AI and ML?

Artificial Intelligence is the broader concept of machines performing tasks that need human intelligence. Machine Learning is a subset of AI where systems learn from data instead of being explicitly programmed.

What will I learn in the AI/ML course?

Python, math for ML, supervised and unsupervised learning, deep learning, neural networks, NLP, computer vision, and an introduction to generative AI.

Is a strong math background required?

Basic knowledge of algebra, statistics, and probability is helpful. We cover the required math during the course.

What are the career opportunities in AI/ML?

AI Engineer, ML Engineer, NLP Engineer, Computer Vision Engineer, and AI Research Associate.

Do you offer online and offline classes?

We are offering only offline classroom training.

Will I receive a certificate?

Yes, you'll receive a certificate of completion after successfully finishing the course and projects.

Do you provide placement assistance?

We offer resume building, mock interviews, and job referrals. Please note that we assist with the process, but we cannot guarantee a job.

Are demo classes available?

Yes, you can attend a free demo class before enrolling.

Swyam Prabha IT Academy Pvt. Ltd.
Your AI / ML career starts with one decision.

Learn. Build. Practice. Get ready for the industry.

Enquire About AI / ML → View Full Curriculum