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AI, Data Science, Engineering and Analytics – Beginner to Expert Program

This course is designed to cater a learner’s need to learn all about AI, Data Science and Analytics.Get the best online Data Science course training from top data scientists.

Key Features

  • Total Session Hours : 60-70 hrs
  • Duration : 12 weeks
  • Efforts : 15 hrs/week
  • Live Projects/Demos : 20+
  • Certification and Job Assistance
  • Flexible Schedule
  • Lifetime free upgrade
  • 24 x 7 Lifetime Support & Access
  • Certified by top MNCs
  • Cloud computing enabled real industry projects
  • CV and Profile revamp
  • Job referrals and assistance
  • Internship opportunities with industry interface
  • Kaggle exposure and path for becoming grandmaster

Next Batch : 27th June – Almost filled up. Register for free Webinar on the course introduction here

Below is the curriculum of the course:

Introduction to Data Science & Analytics-

What is data science/analytics and why is it so important?
Applications of data science/analytics
Different kinds of data science/analytics tools
Data science/Analytics project methodology
Case study

Fundamentals of R & Python

Basics of R and Python
Conditional and Loops
String and List objects
Functions and OOP’s concepts
Exception handling
Database Programming
*Sessions on R are not live , but self paced
Practice assignment

Data Wrangling

Reading CSV, JSON, XML and HTML files using Python
Numpy and Pandas
Relational Databases and Data manipulation with SQL
Scipy Libraries
Loading, Cleaning, Transforming, Merging and Reshaping Data

Statistics and Probability

Descriptive Statistics and Data Distributions
Probability Concepts and Set Theory
Probability Miss Functions
Probability Distribution Functions
Cumulative Distribution Functions
Modelling Distributions
Inferential Statistics
Estimation
Hypothesis Testing
Implementation of Statistical Concepts in Python

Machine Learning Models in Python

Building Models using below Algorithms
Linear and Logistic Regression
Decision Trees
Support Vector Machines (SVMs)
Random Forests
XGBoost (most popular in Kaggle competetions)
K Nearest Neighbour and Hierarchical Clustering
Principal Component Analysis
Text Analytics and Time Series Forecasting

Data Visualization Using Matplotlib and Tableau

Interactive Visualizations with Matplotlib
Data Visualizations using Tableau
Tableau Dashboard and Story Board
Tableau and R integration
*Session on Tableau are not live , but self paced

Deep Learning using Tensorflow

Basics of Neural Network
Linear Algebra
Implementation of Neural Network
Basics of Tensorflow
Convulutional Neural Network (CNNs)
Recurrent Neural network (RNNs)
Generative Models
Semi-Supervised Learning using GAN
Seq-to-Seq Model
Encoder and Decoder

Deploying ML Models using Flask (On Premises) & Cloud

What is Apache Flask
What are Various Cloud Providers – Azure, AWS, Google Cloud etc
How to prepare a model
How to save a model
Deploy a model using Apache Flask
Deploy a model using Cloud

Spark and Data Engineering 

Spark architecture

Data engineering in Spark- RDD, Dataset, Dataframe

Spark ML Lib – Machine learning at Scale

INDUSTRY PROJECTS

We Provide 4-5 Projects during the course , and various industry level use case for practice.
Students get one to one mentoring while solving various industry level projects too
Real world industry Projects and deployment in AWS and Azure Cloud

Sample Certificate:

 

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TAKE THIS COURSE
  • 28,000.00 7,500.00
  • 3 months
  • Course Certificate
24 STUDENTS ENROLLED
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