Tag: Data Science

You will learn about Basics of Python Programming and its features

You will learn and explore on Cloud Client Libraries in GCP

You will get to know the use of Python in Data Science

You will learn on working with ML application using Python

Requirements

• If you have an understanding of Basic Python Programming
• And Working Knowledge on GCP Cloud services

Description

If you are looking for building the skills on Python programming along with Machine learning, Data science and use of Python in cloud platforms, then this is the course for you!

This course takes you through hands-on approach with python programming using IDLE (Python 3.11 64-bit)

Python is an interpreted, high-level and general-purpose programming language. Python is easy to learn and it is powerful programming language. Python has syntax that allows developers to write programs with fewer lines compared to other programming languages

In this course you will learn about Python and its features, data types and data structures in Python. Looping and conditional statements, functions and modules.

You will also learn the OOPs concept of Python, decorators, generators, exception handling and file handling in Python

In this course you will learn to use the Python Libraries in GCP.

And how to use Python in Machine Learning and Data Science.

Our focus is to teach topics that flow smoothly. The course teaches you everything you need to know about python programming with hands-on examples

This course gives a quick introduction to python programming with an emphasis on its activity lessons

What are you waiting for?

Every day is a missed opportunity.

Hurry up!!!!!!

Who this course is for:

• Developers interested in Mastering Python Programming
• Python Developers
• Data Scientist
• Data Analysts
• Software Developers and Cloud Developers

Course content

Learn basics of python for data science

Requirements

• NO it is completely from Scratch

Description

Learn the Building Blocks of Python for Absolute Beginners

Free Beginner Course going over the fundamentals of Python programming.

In this free, brief course we’ll cover all the fundamentals of building python scripts. We’ll go through everything you need to know and understand in Python (and we’ll do it quick too.) In this one hour course we’ll cover more topics than what many other courses cover in multiple hours. We focus on understanding with examples and scenarios rather than all the technical use cases. Once you have a working knowledge of all the components, all the technical use cases come easy.

Who this course is for:

• Beginners of Python

Course content

3 sections • 16 lectures • 1h 49m total length

Learn first step towards Data Science with all important concept of Numerical Python NumPy in Python For Data Science

Requirements

• Basic knowledge of Python Programming Language

Description

Wanna learn NumPy?

Look no further. This course covers everything from how to install and import NumPy to how to solve complex problems involving array creation, transformations, and random sampling.

Course Structure

The course is presented as a series of on-demand lecture style videos with lots of animated examples, code walkthroughs, and challenge problems to test your knowledge. Go as fast or as slow as you want.

It’s difficult to describe everything around us with just one number. The world is multidimensional. The data we are consuming, product we use on daily basis, from non living organism to living organism require many feature to fully characterise and quantify it.

So if you want to learn about fastest python based numerical multi dimensional data processing framework, which is the foundation for many data science package like pandas for data analysis, sklearn scikit-learn for machine learning algorithm, you are at right place.

This course introduce with all majority of concept of NumPy – numerical python library.

I will teach from what and why of NumPy to all important concept of N dimension data processing

This course covers following topics.

• Why and What NumPy is
• NumPy installation
• Creating NumPy array
• Array indexing and slicing
• Array manipulation
• Mathematical & statistical function
• Linear algebra function
• How to persist NumPy array
• Numpy practical application on Images
• RGB Image to Gray scale conversion
• Apply average and edge detection filter on images

Go to my other course needed for Data Scientists. See you inside course.

Happy learning

Abbosjon Madiev

Who this course is for:

• Data Science Beginners
• Anyone who want to learn how to process N dimensional Data
• Anyone who want to learn Numpy – Numerical Python Library.

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Course content

7 sections • 19 lectures • 1h 50m total length

What is Data Science

Artificial Intelligence vs Machine Learning vs Deep Learning

Data Analysis using Python and R

Data Visualization using Python and R

Data Loading using Python and R

Requirements

• All graduates, Data analysts and business analysts

Description

Interested in the field of Data Science, Machine Learning, Data Analytics, Data Visualization? Then this course is for you!

This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.

We will walk you step-by-step into the World of Data Science. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

Moreover, the course is packed with practical exercises which are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.

And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.

Who this course is for:

• Beginner Python & R developers curious about Data Science

Course content

Understand the intuition behind Artificial Neural Networks

Build artificial neural networks with Tensorflow

Classify images, data using deep learning

Apply Convolutional Neural Networks in practice

Requirements

• Some prior coding experience with python is required.

Description

Neural networks are a family of machine learning algorithms that are generating a lot of excitement. They are a technique that is inspired by how the neurons in our brains function. They are based on a simple idea: given certain parameters, it is possible to combine them in order to predict a certain result. For example, if you know the number of pixels in an image, there are ways of knowing which number is written in the image. The data that enters passes through various “ layers” in which a series of adjusted learning rules are applied by a weighted function. After passing through the last layer, the results are compared with the “correct” results, and the parameters are adjusted.

Although the algorithms and the learning process in general are complex, one the network has learned, it can freeze the various weights and function in a memory or execution mode. Google uses these types of algorithms, for example, for image searches.

There is no single definition for the meaning of Deep Learning. In general, when we talk of Deep Learning, we are referring to a group of Machine Learning algorithms based on neural networks that, as we have seen, are characterized by cascade data processing. The entrance signal passes through the various stages, and in each one, they are subjected to a non-linear transformation. This helps to extract and transform the variable according to the determined parameters (weights or boundaries). There isn’t an established limit for the number of stages that a neural network must contain to be considered Deep Learning. However, it is thought that Deep Learning arose in the 80’s, using a model which had 5 or 6 layers. It was (and is) called the neocognitron and was created by the Japanese researcher Kunihiki Fukushima. Neural networks are very effective in identifying patterns.

An example worth highlighting of the application of Deep Learning is the project carried out by Google and the Universities of Stanford and Massachusetts. It aimed to improve the natural language processing techniques of a type of AI called Recurrent Neural Network Language Model (RNNLM). It’s used for automatic translations and creating subtitles, among other thing. Basically, it builds up phrases word by words, basing each word on the previous one and in this way, it can even write poems.

Module 1

1. Introduction to Deep Learning and TensorFlow

2. Basics of Neural Networks

3. Designing a shallow neural network (Scratch and python) (Project)

4. Deeper neural network using TensorFlow. (Project)

Who this course is for:

• Beginners In Python
• Beginners In Deep Learning
• Beginners In Machine Learning

Course content

Understand the basics of Data Science and Analytics

Understand how to use Python and Scikit learn

Get a good understanding of all buzz words like “Data Science”, “Machine learning”, “Data Scientist” etc.

Requirements

• This course does not have any pre-requisities. All you need is a Windows or a MAC machine.

Description

Are you completely new to Data science?

Have you been hearing these buzz words like Machine learning, Data Science, Data Scientist, Text analytics, Statistics and don’t know what this is?

Do you want to start or switch career to Data Science and analytics?

If yes, then I have a new course for you. In this course, I cover the absolute basics of Data Science and Machine learning. This course will not cover in-depth algorithms. I have split this course into 3 Modules. This module, takes a 500,000ft. view of what Data science is and how is it used. We will go through commonly used terms and write some code in Python. I spend some time walking you through different career areas in the Business Intelligence Stack, where does Data Science fit in, What is Data Science and what are the tools you will need to get started. I will be using Python and Scikit-Learn Package in this course. I am not assuming any prior knowledge in this area. I have given some reading materials, which will help you solidify the concepts that are discussed in this lectures.

This course will the first data science course in a series of courses. Consider this course as a 101 level course, where I don’t go too much deep into any particular statistical area, but rather just cover enough to raise your curiosity in the field of Data Science and Analytics.

The other modules will cover more complex concepts. 

Who this course is for:

• Anyone who wants to learn about Data Science from absolute scratch.
• Anyone who wants to switch or make a career in Data Science and Analytics
• Anyone who is curious to know what is Data Science and what does a Data Scientist do in his/her day job.

Course content

Web scrapping using Python

Requirements

• Basic Python skills

Description

The incredible amount of data on the Internet is a rich resource for any field of research or personal interest. To effectively harvest that data, you’ll need to become skilled at web scraping. The Python libraries requests and Beautiful Soup are powerful tools for the job. If you like to learn with hands-on examples and you have a basic understanding of Python, then this course is for you!

In this course, you would learn how to scrap websites using the Beautiful Soup library. This allows for automation for example getting your latest new articles every morning or getting the latest stock prices from Financial websites. After this course, you would be able to know how to inspect web pages and get relevant information from these websites using python.

Learning objectives:

• Use requests and Beautiful Soup for scraping and parsing data from the Web
• Walk through a web scraping pipeline from start to finish
• Build a script that fetches job offers from the Web and displays relevant information in your console

If you are still not familiar with basic python concept I advise you to check out my other free course on Python basics, on the other hand, if you would like to learn more about Data science and machine learning using python, I would also encourage you to check out my Data science course using Python.

Hope you enjoy this course!

Who this course is for:

• Mid beginner intermediate python users

Course content

A high intensity Python 3 crash course for existing programmers who want to learn Python

Requirements

• WARNING: This is a FAST PACE course
• This course is for you already know an existing programming language
• If you are BRAND NEW to Python this course IS NOT for you

Description

Hello and welcome to the Python 3 Crash Course, 2020 edition! 

This is a high intensity training course for programmers who already know another programming language. If you know Java, C, PHP, JavaScript, or any other language and want to make the leap tp Python.. this is the course for you! 

In this course you’ll learn several Python topics very quickly. We’ll start at the very beginning and install Python, execute a .py file from our command line, explore basic math and work with Python variables.

Then we’ll move onto Python data structures, indexing strings and lists, formatting methods, and file management files.

Lastly we’ll learn about conditionals, loops, list comprehensions, functions, OOP, error handling and Python packages.

If you’re a beginner and you’re brand new to Python, that’s OK. You can still take this course and get a one hour high level overview of what Python can do and how to write Python. But people who will get the most from this course are intermediate developers or developers from other programming languages who want to learn Python.

Otherwise, let’s dive in! 

Who this course is for:

• Programmers who already know a programming language
• Intermediate Python developers who already know the basics
• Anyone who wants a 1 hour Python 3 crash course

Course content

2 sections • 24 lectures • 58m total length

Learn Python with OOP

Requirements

• Basic Python Programming knowledge

Description

Python is powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python code is simple, short, readable, intuitive, and powerful, and thus it is effective for introducing computing and problem solving to beginners. It’s elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms.

Python is an object oriented programming language. Unlike procedure oriented programming, where the main emphasis is on functions, object oriented programming focus on objects. Object-oriented programming (OOP) is a method of structuring a program by bundling related properties and behaviors into individual objects.

In this course, we will learn to implement various OOP concepts such as Creating Classes and Objects, Method Overloading and Overriding, Data Hiding, Data Abstraction, Inheritance and Composition Classes, Customization via Inheritance, Specializing inherited Methods

The aim of this course is to help the student to develop general purpose programming using Python as an OOP language to solve the problems.

The theory, practical experiences and relevant soft skills associated with this course are to be taught and implemented, so that the student demonstrates the industry-oriented outcomes such as: Develop python program to demonstrate use of OOP concepts.

Who this course is for:

• Beginner Python Developers and those who are studying Python in Diploma or Engineering of Computer Science or alike Programme.

Course content

1 section • 5 lectures • 1h 11m total length

Step by step guide on how to create your own Python package and publish it on PyPI

Requirements

• Computer with Internet
• Basic understanding of Python and command line

Description

Any Python developer from beginner to advanced must have had used packages like pandas or numpy on regular basis.

How about we create one such package on our own?

How about we publish our package for everyone else to use?

How about contributing to the open source and make the Python language better?

Let’s get started with this course to accomplish all the above!!

Who this course is for:

• Python developers
• Data Science students
• Computer Science students
• Programmers

Course content

6 sections • 6 lectures • 35m total length