DataScience – ML & DL – Regex Software

DataScience - ML & DL

(Batches Start from 15th Feb & 1st Mar, 2021)

About The Program:

With the belief to build a healthy ecosystem as per the Industry Standards REGex Software brings a Internship/Training Program on “DataScience – Machine Learning & Deep Learning”. We organize Internship/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 “DataScience – 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 are preparing for a coding interview, REGex introduce this course for you.

Timing

06:30 PM – 08:30 PM (MWF)

Platform

Google Meet

Duration

8 Weeks

What People Tell About Us

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
60+ hours on demand Live Video Lectures
Offline Access of Lecture Videos & Notes
24*7 Mentorship Support
Working on Live Projects

Output

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

Our Students Placed In

S. No.	Topic
1	Machine learning
	○ Machine learning applications ○ ML vs DL
2	Data Cleaning & Munging with – Python
	○ 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
3	Understanding all concepts of Machine Learning
	○ What is actual machine learning ○ Various Aspects of Data – type, Variables & Category ○ Machine Learning & its Various types
4	Supervised & Unsupervised machine Learning
5	Understanding with Simple Linear Regression
6	Multiple Linear Regression Intuition
	○ Understanding the P -Value
7	What is Regression with its use cases
	○ Support Vector Regression
8	Decision Tree Regression & Random forest Regression
9	Logistic Regression
	○ K-NN [K-Nearest Neighbors]
10	Support Vector Machines
11	Bayes Theorem
	○ Naive Bayes Intuition
12	Dimension Reduction & Feature Selection
	○ Principal Component analysis (PCA – Theory) ○ PCA with Case-Study ○ Linear Discriminant Analysis(LDA) for Dimension Reduction ○ Feature Selection to Select the Most Relevant Predictors
13	Evaluating classification models performance
	○ Confusion matrix ○ Accuracy Paradox ○ CAP Curve
14	K-Mean Clustering
	○ K-Mean Clustering Intuition ○ K-Mean selecting Numbers of Cluster
15	Reinforcement learning
16	Traditional A/B Testing
17	NLP
	○ NLP Intuition ○ Types of NLP ○ Classification vs Deep Learning Models

S. No.	Topic
18	Understanding about Deep Learning
	○ The Neuron ○ Activation function ○ How Neural network work & learn by itself ○ Gradient Descent ○ Stochastic Gradient Descent ○ Ethics of Deep Learning
19	Deep Dive with ANN
	○ What are convolutional neural networks [CNN] ? ○ CNN Architecture ○ CNN Code preparation ○ Recurrent Neural Networks ○ Several layers ■ ReLLu Operation ■ Pooling ■ Flattering ■ Full Connection
20	Statistics and probability Refresher
	○ Learn about mean, median, mode ○ Variation ○ Standard Deviation ○ Density Functions ○ Probability Mass function ○ Percentiles ○ Covariance & Correlation
21	Computer Vision & OpenCV
	○ What is computer vision & its application ○ Face Detection ■ Adding more features & Categorization ■ Object Detection ■ Image creation ■ Working with Images ■ Working with Vectors ○ Facial Expression Recognition in Code (Binary / Sigmoid / Logistic Regression)
22	Natural Language Processing [ NLP]
	○ What are Vectors ○ Working with word Analogy ○ Text Classification ○ Pre Trained word vectors from word2vec ○ Language Models
23	TensorFlow / Keras
	○ Introduction to Tensorflow / Keras ○ Most used right now ○ Resources gathering ○ Keras dealing with Missing Data ○ Dealing with Categorical data
24	AWS SageMaker
	○ Create your own ML models ○ Deploy ML models ○ AWS Rekognition
25	Capstone Project

Fee Structure

(for each program)

Indian Fee

Price: ₹4999/- (Flat 80% off) => ₹999/-

International Fee

Price: $100 (Flat 70% off) => $30

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