The Machine Learning & Artificial Intelligence Certification Bundle

With 39 lectures, this course will tackle the most fundamental building block of practical data science—data wrangling and visualization. It will take you from a basic level of performing some of the most common data wrangling tasks in R with two of the most important R data science packages, Tidyverse and Dplyr. It will introduce you to some of the most important data visualization concepts and techniques that will suit and apply to your data.

• Read-in data into the R environment from different sources
• Learn how to use some of the most important R data wrangling & visualization packages such as Dpylr and Ggplot2
• Carry out basic data pre-processing & wrangling in R studio
• Gain proficiency in data pre-processing, wrangling & data visualization in R

Software not included

This 5-hour course is created to take you by hand and teach you how to tackle the most fundamental building blocks of practical data science: data wrangling and visualization. It will equip you to use some of the most important Python data wrangling and visualization packages such as Seaborn. You will also be able to decide which wrangling and visualization techniques are best suited to answer your research questions and applicable to your data and interpret the results.

• Access 49 lectures & 5 hours of content 24/7
• Understand the most fundamental building blocks of practical data science
• Be equipped w/ some of the most important Python data wrangling & visualization packages
• Implement different techniques on real-life data
• Learn a new concept or technique after each video

Note: Software not included

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

Note: Software not included

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 give 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

Note: Software not included

This course is your complete guide to the 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 61 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

Note: Software not included

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 116 lectures & 12 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

Note: Software not included

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

Note: Software not included

This course will help you learn machine learning terminology and processes with up-to-date knowledge. In this course, you’ll learn and practice framing machine learning problems, data sets, data visualizations, evaluation, and more. You will also get complete resources and applicable codes in this course.

• Access 26 lectures & 3 hours of content 24/7
• Understand basic machine learning terminology & process
• Learn how to frame a machine learning problem & when to use machine learning
• Prepare & develop data sets