Machine Learning & Deep Learning

About The Program:

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

REGex Software Services’s “Machine Learning & Deep Learning” course is a valuable resource for beginners and experts. This course will introduce you to Classification, Clustering Algorithm and Working on Object Detection & Image Recognition from Basics to Advance. If you want to become Data Scientist, REGex introduce this course for you.

What People Tell About Us

What you will Learn

  • Python basics, Machine Learning & Deep Learning
  • Supervised and Unsupervised Learning
  • Regression, Classification and Clustering Algorithms
  • Working of Regression and Classification Algorithms from scratch
  • Deep Learning and Working of Deep Neural Networks
  • Image Recognition & Object Detection
  • Handwritten Digit Recognition

Study Material

  • E-Notes
  • Assignments per day
  • Poll test per day
  • Weekly Tests
  • 80+ hours on demand Live Video Lectures
  • Offline Access of Lecture Videos & Notes
  • 24*7 Mentorship Support

  • Working on Live Projects


  • Able to think out of the box
  • Understand different types of machine learning: Supervised, Unsupervised & Reinforcement
  • Understand working of ML models and neural networks from scratch
  • Build and save your ML models using scikit learn
  • Build projects such as handwritten digit recognition, image Classification, object detection etc
  • Build Deep Neural Networks using Tensorflow
  • Work on more then 10 Use CASES
  • 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 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
  • About Traditional A/B Testing
  • 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

  • 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)

  • 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

  • Create your own ML models
  • Deploy ML models
  • AWS Rekognition
  • Industry Based Project related to DataScience
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