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Why Take This Course?

If you are not learning technology of today then you can’t be part of a successful workforce of tomorrow. With our online certification courses you just don’t master the technology, you also get recognition. We are sure that this will get you the dream job and a shining that you want to have. Our online courses will provide you a personalized learning experience giving you enough flexibility of schedule/time and saving a lot of money and time for you.

An application of AI, it adds immense processing power of computational data, placing the machine in a decision making position.

Why Join Us ?


Expert Team

Our courses are developed by team of experts who have
real expertise in the same domain. They know what is
best for you and how to deliver it to you. Courses are
finalized after proper scruiting, minimising chances
of any error.

Consistent Guidance & Doubt Resolution

Our expert team would always be there for you to
clear any doubt regarding subject matter or providing
you guidance regarding the course you are taking.

Personalized Learning

You can watch our videos in your laptop/tablet at your
homes whenever you want. Saving lot of time and money
that you would be spending at coaching centres in megacities.

One Stop Solution

We will be providing everything that you need to
develop relevant skills and lead a path to success
in corporate world.

Advance your Career

This program was built to ensure that you learn the right skills which are necessary to succeed.

Be Right On Target

With our accurate and guidance scientific teaching methods you can acquire skills and knowledge which is essential for making you what you want to be.

Learn with Ease

You don’t have to go through tons of coaching materials and lots of stress analyzing what to study from where to study. Learn with us sitting at your homes in comfort what is essential.

Learning Community

By joining groups you can have peers to learn and compete with. It certainly helps in assessing yourself and eases the path to success.

Course Curriculum

Machine Learning with Python
Applications of Machine Learning 00:00:00
Why Machine Learning is the Future 00:00:00
Supervised vs. Unsupervised Learning, and Train/Test 00:00:00
K-Means Clustering 00:00:00
Measuring Entropy 00:00:00
Install GraphViz Ensemble Learning Support Vector Machines
More Data Mining and Machine Learning Techniques 00:00:00
Using KNN to predict a rating for a movie 00:00:00
Dimensionality Reduction; Principal Component Analysis 00:00:00
PCA Example with the Iris data set 00:00:00
Data Warehousing Overview: ETL and ELT 00:00:00
Apache Spark: Machine Learning on Big Data
Spark Introduction 00:00:00
Introducing MLLib 00:00:00
K-Means Clustering in Spark 00:00:00
Using the Spark 2.0 DataFrame API for MLLib 00:00:00
Machine learning for Spam detection
Machine Learning: Why should you jump on the bandwagon? 00:00:00
Plunging In – Machine Learning Approaches to Spam Detection 00:00:00
Spam Detection with Machine Learning Continued 00:00:00
Natural Language Processing and Python
Applying ML to Natural Language Processing 00:00:00
Natural Language Processing with NLTK 00:00:00
Natural Language Processing with NLTK – See it in action 00:00:00
Python Drill : Autosummarize News Articles 00:00:00
Document Distance using TF-IDF 00:00:00
Support Vector Regression (SVR)
SVR Intuition 00:00:00
SVR in Python 00:00:00
SVR in R 00:00:00
Ensemble Machine Learning
Ensemble Learning Methods Introduction Bagging Part 00:00:00
Random Forests 00:00:00
Gradient Boosting Machine 00:00:00
Project HR – Human Resources Analytics 00:00:00
Ensemble of ensembles 00:00:00
Building a Machine Learning Web Service
Building a Machine Learning Web Service Concepts 00:00:00
Building a Machine Learning Web Service Code 00:00:00
Practical Machine Learning
Hyperparameters and Cross-Validation 00:00:00
Comparison to Deep Learning 00:00:00
Regression with Sci-Kit Learn is Easy 00:00:00
Random Forest Regression
Random Forest Regression Intuition 00:00:00
How to get the dataset 00:00:00
Random Forest Regression in Python 00:00:00
Random Forest Regression in R 00:00:00



How can I get this course?

Click over “BUY NOW” Button given below the course, you will be redirected to online payments page. Payment can be done online via different modes of payment like Credit Cards, Debit Cards, Net Banking, E-wallets etc.

What do I get?

Depends upon what you choose. You can either enroll for live classes or purchase a tablet/SD card course.

How are live classes different from SD card/tablet courses?

Live classes will be streamed online through our platform on scheduled time. You can watch the streaming session along with other learners, ask your doubts immediately and get them resolved. While tablet/SD card courses are pre-recorded which you can watch whenever you want. Although live streamed videos will remain available to watch-re watch upto a certain time period.

How do I get my doubts resolved?

In live classes you can ask your doubts immediately where it will be resolved by our experts immediately. In case of tablet/SD card courses you can mail your doubts/questions to the mail id provided. Your doubts will be resolved within 24 hours by our experts.

How many number of times can I watch the videos?

You can watch video courses as many times as you want, during the validity duration of the course. However, after the validity period videos will get vanished from SD card/tablet. Although SD card/tablet will be yours to use onwards.

What will be the validity duration of courses?

It will be mentioned for each course. You’ll have the options to choose any of the mentioned plans.

What if I miss my scheduled live class?

No worries at all. Each and every video will be available after each live session. You can watch that when you get time. You can also watch them any number of time during the course duration.

Do I need some software or special tool to play lecture videos?

No. You can play our videos on regular video players. Also our live courses will be streamed on our website so you don’t need any special software or tool for that.

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