Machine Learning & Data Science Certification Training Bundle

This course is your complete guide to practical machine and deep learning using the Tensorflow and Keras frameworks in Python. In the age of Big Data, companies across the globe use Python to sift through the avalanche of information at their disposal and the advent of Tensorflow and Keras is revolutionizing deep learning. This course will help you break into this booming field.

• 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

This course is your complete guide to practical data science using the Tensorflow framework in Python. Here, you’ll cover all the aspects of practical data science with Tensorflow, Google’s powerful deep learning framework used by organizations everywhere.

• 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

This course offers a complete guide to practical data science using Python. You’ll cover all aspects of practical data science in Python. By storing, filtering, managing, and manipulating data in Python, you can giver your company a competitive edge and boost your career to the next level.

• 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, barplots, pie/line charts, & more

In this easy-to-understand, hands-on course, you’ll learn the most valuable Python Data Science basics and techniques. You’ll discover how to implement these methods using real data obtained from different sources and get familiar with packages like Numpy, Pandas, Matplotlib, and more. You’ll even understand deep concepts like statistical modeling in Python’s Statsmodels package and the difference between statistics and machine learning.

• 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

In this course, you’ll use easy-to-understand, hands-on methods to absorb the most valuable R Data Science basics and techniques. After this course, you’ll understand the underlying concepts to understand what algorithms and methods are best-suited for your data.

• 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

Dive into R data science using real data in this comprehensive, hands-on course. Get up to speed with data science packages like caret, h20, MXNET, as well as underlying concepts like which algorithms and methods are best suited for different kinds of data. Help your company scale by becoming an R expert!

• 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

In this course, you’ll learn to implement R methods using real data obtained from different sources. After this course, you’ll understand concepts like unsupervised learning, dimension reduction, and supervised learning.

• 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

In this course, you’ll start by absorbing the most valuable Python Data Science basics and techniques. You’ll get up to speed with packages like Numpy, Pandas, and Matplotlib and work with real data in Python. You’ll even delve into concepts like unsupervised learning, dimension reduction, and supervised learning.

• 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