...

Full Bio

Apart from Dot NET Vs PHP Vs Java. Why Dot NET is the best?

today

Everyone wants a Java Developer. Why?

today

Do you really need to know Programming Language for becoming a Professional Hacker?

yesterday

K2-Artificial Intelligence Technology has been introduced by Tech Mahindra

yesterday

Every Programmer should strive for reading these 5 books

526098 views

Why you should not become a Programmer or not learn Programming Language?

171459 views

See the Salaries if you are willing to get a Job in Programming Languages without a degree?

135255 views

Highest Paid Programming Languages With Highest Market Demand

121974 views

Python Programming Language can easily be acquired easily. How?

106416 views

### Order a bundle having 8 courses, 506 lessons, and 2839 enrollment through which you can also master in Machine Learning

- Access 62 lectures & 5 hours of content 24/7
- Get a full introduction to Python Data Science
- Get started w/ Jupyter notebooks for implementing data science techniques in Python
- Learn about Tensorflow & Keras installation
- Understand the workings of Pandas & Numpy
- Cover the basics of the Tensorflow syntax & graphing environment and Keras syntax
- Discover how to create artificial neural networks & deep learning structures w/ Tensorflow & Keras

- Access 62 lectures & 5 hours of content 24/7
- Get a full introduction to Python Data Science
- Get started w/ Jupyter notebooks for implementing data science techniques in Python
- Learn about Tensorflow installation & other Python data science packages
- Understand the workings of Pandas & Numpy
- Cover the basics of the Tensorflow syntax & graphing environment
- Learn statistical modeling w/ Tensorflow
- Discover how to create artificial neural networks & deep learning structures w/ Tensorflow

- Access 50 lectures & 6 hours of content 24/7
- Get a full introduction to Python Data Science & Anaconda
- Cover basic analysis tools like Numpy Arrays, Operations, Arithmetic, Equation-solving, Matrices, Vectors, & Broadcasting
- Explore data structures & reading in Pandas, including CSV, Excel, JSON, and HTML data
- Pre-process & wrangle your Python data by removing NAs/No data, handling conditional data, grouping by attributes, etc.
- Create data visualizations like histograms, boxplots, scatterplots, bar plots, pie/line charts, & more

- Access 117 lectures & 11 hours of content 24/7
- Get a full introduction to Python Data Science & Anaconda
- Cover basic analysis tools like Numpy Arrays, Operations, Arithmetic, Equation-solving, Matrices, Vectors, & Broadcasting
- Explore data structures & reading in Pandas, including CSV, Excel, JSON, and HTML data
- Pre-process & wrangle your Python data by removing NAs/No data, handling conditional data, grouping by attributes, etc.
- Create data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, & more
- Discover how to create artificial neural networks & deep learning structures

- Access 52 lectures & 5 hours of content 24/7
- Get an introduction to powerful R-based packages for time series analysis
- Learn commonly used techniques, visualization methods & machine/deep learning techniques that can be implemented for time series data
- Apply these frameworks to real life data including temporal stocks & financial data

- Access 51 lectures & 5 hours of content 24/7
- Get introduced to powerful R-based deep learning packages such as h2o & MXNET
- Explore deep neural networks (DNN), convolution neural networks (CNN) & recurrent neural networks (RNN)
- Learn to apply these frameworks to real life data for classification & regression applications

- Access 66 lectures & 7.5 hours of content 24/7
- Learn how to harness the power of R for practical data science
- Read-in data into the R environment from different sources
- Carry out basic data pre-processing & wrangling in R studio
- Implement unsupervised/clustering techniques such as k-means clustering
- Explore supervised learning techniques/classification such as random forests
- Evaluate model performance & learn best practices for evaluating machine learning model accuracy

- Access 46 lectures & 4 hours of content 24/7
- Harness the power of Anaconda/iPython for practical data science
- Carry out basic data pre-processing & wrangling in Python
- Implement dimensional reduction techniques (PCA) & feature selection
- Explore neural network & deep learning based classification