The A to Z Data Science & Machine Learning Bundle

As you already know, the artificial intelligence domain is divided broadly into deep learning and machine learning. In fact, deep learning is machine learning itself, but deep learning with its deep neural networks and algorithms try to learn high-level features from data without human intervention. That makes deep learning the base of all future self intelligent systems.

This course will start from the very basic things like learning the programming language basics and other supporting libraries first, then proceed with the core topic. After the basics, you will then install the deep learning libraries theano, TensorFlow, and the API for dealing with these called Keras. We will be writing all our future codes in Keras.

• Access 81 lectures & 5 hours of content 24/7
• Learn the programming language basics & other supporting libraries
• Prepare your computer for Python coding
• Study topics including Python assignment, flow control, functions list & typles, dictionaries, functions, etc
• Learn the basics of the Matplotlib library
• Write your codes in Keras

If you're someone who's interested in learning what's actually happening behind the scenes of Machine Learning, this is the course for you. It will discuss the basics of Machine learning and the Mathematics of Statistical Regression which powers almost all the Machine Learning Algorithms. You will work on exercises for regression in both manual plain mathematical calculations and then compare the results with the ones we got using ready-made python functions.

• Access 65 lectures & 5 hours of content 24/7
• Discuss the components included in Machine Learning & Anaconda
• Learn the very basics of Python programming language
• Have a quick peek of the Python library called NumPy
• Try statistics techniques called variance & standard deviation
• Implement a python program to find both these values for the same dataset & verify the results

This course will teach you how to get started with Numpy. Python was described as a language that is capable of doing anything and everything. The good news about this course is that you don’t need advanced knowledge in Python or in any programming language. All you need is just simple knowledge of how to create simple Python functions/scripts.

Learning NumPy is very important if you want to either use it in machine learning and data science or on its own for mathematical purposes. And this course gives you the opportunity to learn that in a simple step-by-step approach. By the end of this course, you will have learned how to use NumPy, how to create arrays, matrices, call, mathematical, and statistical functions of NumPy.

• Access 29 lectures & 1 hour of content 24/7
• Learn how to install Python
• Create Python files
• Use NumPy
• Create arrays in NumPy
• Apply mathematical and statistical functions to arrays & matrices

Analyzing data and building machine learning models is one thing. Packaging these analyses and models such that they are sharable is a different ball game altogether. This course aims at teaching you the fastest and easiest way to build and share data applications using Streamlit. You don’t need any experience in building front-end applications for this. At the end of the course, you will have built several applications that you can include in your data science portfolio. You will also have a new skill to add to your resume.

• Access 151 lectures & 9.5 hours of content 24/7
• Learn the basics of Python & NumPy
• Be familiar with Streamlit
• Integrate Matplotlit & Seaborn in Streamlit
• Using Altair & Vega-Lite in Streamlit
• Understand all Streamlit widgets

In this practical, hands-on course, you’ll learn how to program in R and how to use R for effective data analysis, and visualization, and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.

The course covers practical issues in statistical computing which include programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting on R code. Blending practical work with solid theoretical training, we take you from the basics of R Programming to mastery.

• Access 70 lectures & 22 hours of content 24/7
• Become a professional Data Scientist, Data Engineer, Data Analyst, or Consultant
• Write complex R programs for practical industry scenarios
• Learn data cleaning, processing, wrangling, & manipulation
• Learn Plotting in R (graphs, charts, plots, histograms etc)

Learn the basics of Python Data Structures and the most important Data Science libraries like NumPy and Pandas with step-by-step examples! It will cover an introduction to Python, setting and configuring on your PC, discussing basic data structures, and more. Overall, this course is a perfect starter pack for your long journey ahead with big data and machine learning. By the end of this course, you will have learned Python essentials for Data Science.

• Access 63 lectures & 6.5 hours of content 24/7
• Get an introduction to Python, its applications, & the libraries
• Be familiar with Anaconda, Jupyter notebook, & data structures
• Learn data structures in Python like lists, tuples & sets
• Import CSV & JSON file data as a dataframe to do the operations and later convert this dataframe to either CSV and JSON objects
• Concatenate, join, & merge two pandas dataframes
• Use Matplotlib library to generate graphs & plots
• Use Histogram to group numbers into ranges

Everyone wants to excel at machine learning and data science these days — and for good reason. Data is the new oil and everyone should be able to work with it. However, it’s very difficult to become great in the field because the latest and greatest models seem too complicated. “Seems complicated” — but they are not! If you have a thorough understanding of probability and statistics, they would be much, much easier to work with! And that’s not all — probability is useful in almost all areas of computer science (simulation, vision, game development, and AI are only a few of these). If you have a strong foundation in this subject, it opens up several doors for you in your career!

That is the objective of this course: to give you the strong foundations needed to excel in all areas of computer science — specifically data science and machine learning. The issue is that most of the probability and statistics courses are too theory-oriented. They get tangled in the maths without discussing the importance of applications. Applications are always given secondary importance.

• Access 37 lectures & 6.5 hours of content 24/7
• Learn necessary concepts in statistics & probability
• Know important concepts in the subject necessary for Data Science and/or Machine Learning
• Understand distributions & their importance
• Study Entropy – the foundation of all Machine Learning
• Get an introduction to Bayesian Inference
• Apply concepts through code