Odissitech

Explore how we use Machine Learning to uncover insights, predict outcomes, and automate decision-making using real-world data.

What We Do

Machine Learning In Action

Machine Learning empowers computers to learn patterns from data and make intelligent decisions with minimal human input using statistical models and algorithms.

Pattern Recognition

Identify trends and anomalies in data for smarter, data-backed decisions.

Predictive Modeling

Build models that forecast future outcomes based on historical patterns and variables.

Automation & Optimization

Automate complex tasks and refine operations by leveraging machine-learned insights.

📘 Course Curriculum

Machine Learning & Deep Learning

A structured journey from Python basics to advanced deep learning and job preparation.

Module 01

Introduction to Python

Learn the basics of Python programming, including syntax, variables, and data types.

Module 02

Conditionals & Loops

Control program flow using if-else statements and looping constructs.

Module 03

Functions

Create reusable code blocks using functions with parameters and return values.

Module 04

OOPs Concept

Explore classes, objects, inheritance, and encapsulation in Python.

Module 05

Intro to ML & Its Types

Get an overview of Machine Learning and its core types.

Module 06

Regression

Predict continuous outcomes using regression models.

Module 07

Classification

Categorize data using logistic regression, SVM, and decision trees.

Module 08

Bagging

Improve accuracy using Random Forest and ensemble methods.

Module 09

Boosting

Use AdaBoost and XGBoost to refine weak models.

Module 10

Clustering

Group data points with K-Means and Hierarchical clustering.

Module 11

Intro to Deep Learning

Understand how neural networks mimic the human brain.

Module 12

Perceptron

Study the simplest unit of neural networks.

Module 13

Forward Propagation

See how input data moves through the network.

Module 14

Artificial Neural Networks

Build and train multi-layer neural networks.

Module 15

Loss & Cost Function

Measure and reduce model error using cost functions.

Module 16

Activation Function

Add non-linearity to networks with activation functions.

Module 17

Optimizers

Update model weights using SGD, Adam, and RMSprop.

Module 18

Convolutional Neural Networks

Analyze visual data using CNNs for image recognition.

Module 19

Recurrent Neural Networks

Model sequential data like text or time series.

Module 20

LSTM

Handle long dependencies in sequences with LSTM.

Module 21

Projects

Work on real-world problems using live datasets.

Module 22

Resume Building

Create a strong resume for roles in ML and data science.

Module 23

Mock Test & Interviews

Practice with mock assessments and interviews.

Contact Us

Our Location

Machine Learning