★★★★★

(2,095 ratings) 50,000+ Student

AI WITH MACHINE & DEEP LEARNING

AI with Machine Learning and Deep Learning represents the integration of data-driven, learning-based techniques into artificial intelligence systems, enabling them to perform complex tasks and make intelligent decisions across various domains and industries.

Radical Technologies

50,000+ English 60 hours Weekdays / Weekends Classroom / Online / Corporate

Online / Classroom

IT Training Programme

Duration 60 hours

Batch Type Weekdays / Weekends

Mode of Training Classroom / Online / Corporate

Locations Pune, Bangalore, Kochi

Language English

Certification Globally Recognized

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100% placement assistance

What you'll learn

Understand core concepts and architecture from the ground up

Get hands-on with the tools used by working professionals

Build real-world projects you can add to your portfolio

Learn industry best practices and coding standards

Practice with real datasets and real-world scenarios

Prepare for certification and technical interviews

Work on collaborative, team-based exercises

Apply performance tuning and optimization techniques

Understand how the technology fits into a larger ecosystem

Complete assignments reviewed by mentors

Programme Overview

18 sections covering the complete curriculum — a single, progressive learning arc.

60 hours

Training Duration

Core Modules

115

Total Lessons

4.4★

Average Rating

50K+

Students Trained

Foundations & Core Concepts

Get hands-on with the fundamentals and architecture — the building blocks for everything that follows.

Fundamentals Architecture Setup

Hands-On Practical Training

Work through real exercises and assignments designed to mirror what you will do on the job.

Practicals Assignments Labs

Real-World Projects

Apply what you have learned to end-to-end projects that go straight into your portfolio.

Projects Portfolio Case Studies

Advanced Techniques

Go beyond the basics with advanced concepts, integrations and production-grade practices.

Advanced Integration Best Practices

Ecosystem Integration

Understand how this technology connects with the broader tools and platforms used in the industry.

Ecosystem Tools Platforms

Performance & Interview Prep

Master optimization techniques and prepare for the technical interview questions employers actually ask.

Optimization Interview Prep Certification

Who is this programme for?

Whether you're already writing code, working with data, or supporting applications today — this programme is built to take you into a AI role.

Software Developers

Engineers who want to add this skill set to their toolkit

Analysts & Consultants

Professionals moving into a more technical, hands-on role

IT Professionals

System admins and support engineers upskilling into a new domain

Fresh Graduates

CS/IT graduates aiming for a job-ready technical role

Course Curriculum

18 sections • 115 lessons • 60 hours

Jump to Section

Total Duration

60 hours

100% Practical · Unlimited Doubt Sessions

01 Introduction with Artificial Intelligence (Project – 1)

What is AI (Artificial Intelligence) ?

What types of intelligences we are talking about?

Different definitions and Ultimate goal of AI.

What are application areas for AI?

History of AI and some real life examples of AI.

02 ML and other related terms to AI (Project -1)

What is ML and How it is related with AI?

What is NLP and How it is related with AI?

What is DL and How it is related with ML and AI?

What are ANNs and DNNs and How are they related to AI?

03 A working example of AI and ML (Project – 1)

Project 1 – These simple tasks are to make you understand how AI and ML can find their applications in real life.

04 Python libraries for ML (Project – 1)

What are Libraries, packages and Modules?

What are top Python libraries for ML in Python?

05 Setting up Anaconda development environment (Project – 1)

Why choosing Anaconda development environment?

Setting up Anaconda development environment on Windows 10 PC.

Verifying proper installation of Anaconda environment.

06 Getting into core development of ML(Project – 1)

What is a classifier in ML?

Important elements and flow of any ML projects.

Let’s develop our first ML program – explanations

Let’s develop our first ML program – development

07 Different ML techniques (Project – 2)

These simple tasks are going to give you some great experience with Machine Learning introductory programs or better say, “Hello world” programs of Machine Learning.

What all ML techniques are there?

Evaluation methods of all ML techniques.

development

08 Developing complete project of ML (IRIS flower project) (Project – 2)

Developing complete ML project – understanding data set

Developing complete ML project – understanding flow of project

Developing complete ML project – visualizing data set through Python

Developing complete ML project – development

Developing complete ML project – concepts explanations

Developing another project of ML (Digit recognition project)

09 Introduction of Ai with Deep Learning (Project – 3)

After completing these project, you have done and understood multiple complete projects of Machine Learning.

Installation

CPU Software Requirements

CPU Installation of PyTorch

PyTorch with GPU on AWS

PyTorch with GPU on Linux

PyTorch with GPU on MacOSX

first ML program – development

10 PyTorch Fundamentals : Matrices (Project – 3)

Matrix Basics

Seed for Reproducibility

Torch to NumPy Bridge

NumPy to Torch Bridge

GPU and CPU Toggling

Basic Mathematical Tensor Operations

Summary of Matrices

11 Linear Regression with PyTorch (Project – 3)

Linear Regression Introduction

Linear Regression in PyTorch

Linear Regression From CPU to GPU in PyTorch

Summary of Linear Regression

12 Logistic Regression with PyTorch (Project – 3)

Logistic Regression Introduction

Linear Regression Problems

Logistic Regression In-depth

Logistic Regression with PyTorch

Logistic Regression From CPU to GPU in PyTorch

Summary of Logistic Regression

13 Feedforward Neural Network with PyTorch (Project – 3)

Logistic Regression Transition to Feedforward Neural Network

Non-linearity

Feedforward Neural Network in PyTorch

More Feedforward Neural Network Models in PyTorch

Feedforward Neural Network From CPU to GPU in PyTorch

Summary of Feedforward Neural Network

14 Convolutional Neural Network (CNN) with PyTorch (Project – 3)

Feedforward Neural Network Transition to CNN

One Convolutional Layer, Input Depth of 1

One Convolutional Layer, Input Depth of 3

One Convolutional Layer Summary

Multiple Convolutional Layers Overview

Pooling Layers

Padding for Convolutional Layers

Output Size Calculation

CNN in PyTorch

More CNN Models in PyTorch

CNN Models Summary

Expanding Model’s Capacity

CNN From CPU to GPU in PyTorch

Summary of CNN

15 Recurrent Neural Networks (RNN) (Project – 3)

Introduction to RNN

RNN in PyTorch

More RNN Models in PyTorch

RNN From CPU to GPU in PyTorch

Summary of RNN

16 Long Short-Term Memory Networks (LSTM) (Project – 3)

Introduction to LSTMs

LSTM Equations

LSTM in PyTorch

More LSTM Models in PyTorch

LSTM From CPU to GPU in PyTorch

Summary of LSTM

17 Deep Learning Projects (Project – 4)

Churn Modelling using ANN

Mini

Image Classification

Mini

Image classification using Transfer learning

Major

Sentence Classification using RNN,LSTM,GRU

Mini

Sentence Classification using word embeddings

Major

Object Detection using yolo

Major

Note : Depends upon Trainers above projects may vary

18 Machine Learning Projects (Project – 4)

EDA on movies database

Mini

House price prediction using Regression

Mini

Predict survival on the Titanic using Classification

Mini

Image Clustering

Mini

Document Clustering

Mini

Twitter US Airline Sentiment

Major

Restaurant revenue prediction

Major

Disease Prediction

Major

Note : Depends upon Trainers above projects may vary

Tools & Technologies

Every tool listed here is installed, configured and used in a hands-on lab session.

Core Tools

Hands-On Labs

Practical Environment

Industry-Standard Tools

Real-World Setup

Guided Exercises

Skill Building

Sample Datasets

Practice Material

Practice & Projects

Mini Projects

Applied Practice

Assignments

Mentor Reviewed

Doubt Sessions

Live Support

Career Readiness

Resume Building

Career Support

Mock Interviews

Interview Prep

Certification Prep

Global Recognition

Deployment & Delivery

Production Practices

Real-World Ready

Best Practices

Industry Standards

115+

Hands-On Lessons

Core Modules

60 hours

Training Duration

100%

Practical Training

You don't just learn AI WITH MACHINE & DEEP LEARNING. You ship it.

Three major projects, each mirroring how production teams actually work — from guided foundations to a portfolio-ready capstone.

PROJECT // 01

Guided Foundation Project

→Requirement Analysis

→Guided Implementation

→Mentor Review

→Iteration

Foundation Beginner

Apply the fundamentals in a structured, mentor-reviewed project

Take the core concepts from the first half of the curriculum and apply them to a realistic scenario, with guidance and feedback from your mentor at every step.

Structured project brief

Step-by-step implementation

Mentor feedback and review

Documented outcome

Stack Core Concepts Best Practices

PROJECT // 02

Applied Practice Project

→Scenario Design

→Independent Build

→Testing & Validation

→Peer Review

Applied Intermediate

Build a more independent project mirroring real production scenarios

Work through a project that combines multiple concepts from the curriculum, closer to how work is actually structured on the job — less hand-holding, more ownership.

End-to-end implementation

Testing and validation

Documentation

Peer/mentor review

Stack Applied Skills Testing

PROJECT // 03

Capstone Project

→Planning

→End-to-End Build

→Review & Refinement

→Presentation

Capstone Advanced

Take a project from requirements to a polished, portfolio-ready deliverable

Your final project — plan, build, test and present a complete solution using everything covered in the curriculum, reviewed by mentors before you graduate.

Complete working solution

Presentation-ready documentation

Mentor sign-off

Portfolio-ready deliverable

Stack Full Curriculum Portfolio

All 3 projects go directly into your portfolio & resume — reviewed by mentors before you graduate.

See Sample Project Reports

Upcoming Batches

No upcoming batches scheduled right now. Enquire to get notified.

Why Radical Technologies

Live Online Training

Highly practical oriented training
Installation support on your system
24/7 Email and Phone support
100% Placement Assistance
Global Certification Preparation
Trainer-Student Interactive Portal
Assignments and Projects by Mentors

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Live Classroom Training

Weekend / Weekdays / Morning / Evening batches
80:20 Practical and Theory ratio
Real-life Case Studies
Easy make-up for missed sessions
PSI | Kryterion | Redhat Test Centers
Lifetime Video Classroom Access (coming soon)
Resume Prep and Mock Interviews

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Self-Paced Training

Learn 300+ courses at your own time
50,000+ Satisfied Learners
Course Completion Certificate
Practical Labs available
Mentor Support available
Doubt Clearing Session available
10% Discounted Global Certification

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Global Certification

Radical Technologies is the leading IT certification institute in Pune, offering globally recognized certifications across various domains. With expert trainers and comprehensive materials, we ensure students gain in-depth knowledge and hands-on experience to excel in their careers. Our certification programs are tailored to meet industry standards — from cloud technologies to data science — empowering individuals to stay ahead in the ever-evolving tech landscape.

Career Services

At Radical Technologies, we are committed to your success beyond the classroom. Our 100% Job Assistance program ensures that you are not only equipped with industry-relevant skills but also guided through the job placement process. With personalised resume building, interview preparation, and access to our extensive network of hiring partners, we help you take the next step confidently into your IT career.