Machine Learning & Deep Learning Program

About The Program:

With the belief to build a healthy ecosystem as per the Industry Standards REGex Software brings an Industrial Training/Internship Program on “Machine Learning and Deep Learning”. We organize Machine Learning and Deep Learning Program for improving the knowledge and skills of the Students/Professionals, so that they can become specialized in the field of Machine Learning and get their Dream Job in Software Development Field in Big MNCs.

REGex Software Services’s “Machine Learning and Deep Learning” program is a valuable resource for beginners and experts. This program will introduce you to Python, Machine Learning, Deep Learning etc. from Basics to Advance. If you want to become Machine Learning Expert, REGex introduce this program for you.


Offline: 01:00 PM – 03:00 PM //
04:00 PM – 06:00 PM
Online: 09:00 PM – 11:00 PM


Physical (Jaipur)
Online (Google Meet)


10 – 12 Weeks


30 per Batch

Pause Your Program

You can pause your program if you have any Medical Emergency or if you have Exams and join again our new program within next 3 Months

What People Tell About Us

What you will Learn


Duration: 25 - 30 Hours

Machine Learning

Duration: 35 - 40 Hours

Deep Learning

Duration: 35 - 40 Hours

Study Material

  • E-Notes
  • Assignments & Poll test
  • 100+ hours on demand Live Video Lectures
  • Access of Recordings & Study Material
  • Mentorship Support
  • Work on multiple Minor Projects & Use Cases
  • Work on Live Projects


  • Able to think out of the box
  • Become expert in multiple technology domains like: Python, Machine Learning, Deep Learning
  • Understand working of ML models deployment with AWS
  • Build projects on multiple technology domains
  • Work on Use CASES & Projects
  • Learn to deploy your models on AWS Sagemaker or Google cloud Platform

Live Sessions

Live Sessions by Expertise Trainers and Access of Recorded Session is also available

Live Projects

Get a chance to work on Industry Oriented Projects to implement your learning

24*7 Support

24*7 Mentorship Support available for all Students to clear all of your doubts


REGex provides Internship / Job opportunities to the best Students in different Companies.

Our Students Placed In


Course Content

Machine Learning

  • Machine learning applications
  • ML vs DL
  • Basics of Python [Syntax]
  • Working with Pandas, Numpy & Matplotlib
    ■ Working with Missing Data
    ■ Data Grouping
    ■ Data Subsetting
    ■ Merging & Joining Data Frames
  • Importing Libraries & Datasets
  • Munging & handling missing data
  • Splitting the dataset into Training set & Test set
  • What is actual machine learning
  • Various Aspects of Data – type, Variables & Category
  • Machine Learning & its Various types
  • About Supervised & Unsupervised Learning
  • Understanding Simple Linear Regression
  • Understanding the P -Value
  • Support Vector Regression
  • K nearest neighbours
  • Logistic regression
  • Naive Bayes
  • Decision trees
  • Random forests
  • Bagging Boosting
  • Maximum likelihood classifier
  • Support vector machines
  • Principal Component analysis (PCA – Theory)

  • PCA with Case-Study

  • Linear Discriminant Analysis(LDA) for Dimension Reduction

  • Feature Selection to Select the Most Relevant Predictors

  • Confusion matrix

  • Accuracy Paradox

  • CAP Curve

  • K-Mean Clustering Intuition

  • K-Mean selecting Numbers of Cluster

  • About Reinforcement Learning

Deep Learning

  • NLP Intuition

  • Types of NLP

  • Classification vs Deep Learning Models

  • The Neuron

  • Activation function

  • How Neural network work & learn by itself

  • Gradient Descent

  • Stochastic Gradient Descent

  • Ethics of Deep Learning

  • What are convolutional neural networks [CNN] ?

  • CNN Architecture

  • CNN Code preparation

  • Recurrent Neural Networks

  • Several layers

    ■ ReLLu Operation

    ■ Pooling

    ■ Flattering

    ■ Full Connection

  • Statistics

  • Sample Selection

  • Probability Theory

  • Hypothesis

  • Model Relationship

  • Model Fit

  • Descriptive Statistics

  • Types of Data

  • Qualitative Data

  • Histograms

  • Different Plots

  • Centrality and Spread

  • Outliers

  • Median, Mean, Mode

Computer Vision

  • What is computer vision & its application

  • Face Detection

    ■ Adding more features & Categorization

    ■ Object Detection & Image creation

    ■ Working with Images & vectors

  • Facial Expression Recognition in Code (Binary / Sigmoid /Logistic Regression)

  • Object detection

  • Neural style transfer

  • YOLO

  • RCNNs

  • Resnet 50

  • Tensorboard

  • What are Vectors

  • Working with word Analogy

  • Text Classification

  • Pre Trained word vectors from word2vec

  • Language Models

  • Introduction to Tensorflow / Keras

  • Most used right now

  • Resources gathering

  • Keras dealing with Missing Data

  • Dealing with Categorical data

Model Deployment & Projects

  • Create your own ML models
  • Deploy ML models
  • AWS Recognition
    • Titanic Classification
    • Carbon Emissions
    • Car price analysis
    • Drug Prediction
    • Hand written Digit Recognition
    • CIFAR 10
    • CIFAR 100
    • Cats vs dogs
    • Intel scene Classification
    • Transfer Learning
    • Object Detection

Extra Sessions

Additinal Session on GIT, Linux, Docker, AWS Basics, Jenkins and many more for all students.

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