Tag: Data Science

Learn Python programming for a career in machine learning, data science & web development.

Requirements

• You should have a basic understanding of Computer Programming terminologies. A basic understanding of any of the programming languages is a plus.
• A computer – Windows, Mac, and Linux are all supported. Setup and installation instructions are included for each platform.
• Your enthusiasm to learn this go-to programming language. It’s a valuable lifetime skill which you can’t un-learn!

Description

Python programming language is one of the most popular programming languages in the world right now. With the ease of access and easier implementations of complex-looking tasks, the Python programming language has made its mark in the IT industry. The number of developers switching to Python evident that people all around the globe are adamant to learn Python.

The scalability of the Python programming language is immense and can be implemented almost in every domain. Data Science has reached another milestone with Python, and the amount of data flowing in each year, Python came as a savior and helped achieve revolutionary developments in the Data Science market.

The amount of jobs created each year has also taken a hockey stick growth with an expected 2 million jobs in the year 2020 alone. And this is just the case with Data Science when it comes to other career paths, Python is equally desirable by any other organization. The giants of the industries like Amazon, Facebook, Instagram, YouTube, etc, are just the tip of the iceberg. When we explore the Python job market, there is an expected increase in the jobs related to Data Science, which would amount to close to 700,000 jobs in the year 2020 alone.

Getting Started With Python

Starting your journey in Python requires that you are familiar with how the technology actually works. And the very first thing that you would learn is how you can set up the Python environment on your systems and choose the best IDE that would help you in the best possible ways.

Who this course is for:

• Beginners with no previous programming experience looking to obtain the skills to get their first programming job.
• Programmers switching languages to Python.
• Those who know Python basics and want to master Python

Course content

1 section • 9 lectures • 31m total length

Feel enjoy to review your knowledge about Python Language practically!?

Requirements

• Have previous knowledge about python langauge

Description

There are many of courses and sources to learn python language, but the problem is shortage of  practices compared to the theoretical section.

( Review Python Practically) practices are more than videos. The practices include of guidlines to help the student to master the knowledge. Wasting time until you get boring is not our goal in this course, our goal is to  think step by step  and point by point for ordering the knowledge in our brains gradually.

This course will cover :

• Python Intro
• Python preparation
• Python statements
• Python data types
• Python Function
• File

The codes in the practices are:

1) Codes without inputs

This means codes do not have input function, but have variables assigned to values or with print function

2) Codes with inputs

This means codes have input function, and may have variables assigned to values or print function

Notes:

1) The course have codes with (Error) result  when (Run) the code and that will help you to avoid the errors.

2)  (Input) in the practices will show you how the code operate depends on the inputs in each running time.

To the end of this course, you will have enough background base  to learn python projects easily. The projects you can do by python are many. I have mentioned several common projects in the course in the first section.

Who this course is for:

• Who wants to master his knowledge in short time
• Suitable for student who has studied python before

Course content

10 sections • 89 lectures • 49m total length

Begin the journey with learning the history of Python

Understand how to use and install both the Jupyter Notebook and Pycharm

Have a fundamental understanding of the Python Data types, Conditional Loops, Collections, Functions, Operators and others.

Learn to use Object Oriented Programming with classes!

Acquire the pre-requisite Python skills to move into specific branches – Machine Learning, Data Science, etc

Have the skills and understanding of Python to confidently apply for Python programming jobs.

Learn Python from experienced professional software developer.

Requirements

• Access to a computer with an internet connection.
• Your enthusiasm to learn this go-to programming language!

Description

Become a Python Programmer and learn one of employer’s most requested skills of 2020!

This is the most comprehensive, yet straight-forward, course for the Python programming language on Udemy! Whether you have never programmed before, already know basic syntax, or want to learn about the advanced features of Python, this course is for you! In this course we will teach you Python 3.

With over 50 lectures and more than 6 hours of video this comprehensive course leaves no stone unturned! This course includes quizzes, tests, coding exercises and homework assignments as well as 3 major projects to create a Python project portfolio!

Learn how to use Python for real-world tasks, such as working with PDF Files, sending emails, reading Excel files, Scraping websites for information, working with image files, and much more!

This course will teach you Python in a practical manner, with every lecture comes a full coding screencast and a corresponding code notebook! Learn in whatever manner is best for you!

We cover a wide variety of topics, including:

• Intro to coding
• Intro to Python
• Installing Python
• Installing Pycharm
• Installing Jupyter Notebook
• Running Python Code
• Intro to Variables
• integer
• Boolean and None
• Strings
• String Indexing
• String and Character Functions
• String Formatting
• Arithmetic Operators
• Comparison Operators
• Bitwise Operators
• If Statement
• For Loop
• While Loop
• Lists
• Tuples
• Sets
• Dictionary
• User-Defined Functions
• Lambda Function
• File I/O
• Debugging and Error Handling
• Modules
• Object Oriented Programming
• Inheritance
• Polymorphism
• Advanced Methods
• Unit Tests
• Quizzes
• and much more!

You will get lifetime access to over 50 lectures plus corresponding Notebooks for the lectures!

You will keep access to the Notebooks as a thank you for trying out the course! And the unique thing that why you should chose this course over others is that, is that each video tutorial contains almost all the unique tricks and info that after watching them you won’t need to search for other sources to learn more. After watching videos with full of information you will take quizzes to check whether you have learned particular trick.

So what are you waiting for? Learn Python in a way that will advance your career and increase your knowledge, all in a fun and practical way!

Who this course is for:

• Beginners who have never programmed before.
• Programmers switching languages to Python.
• Intermediate Python programmers who want to level up their skills!

Course content

Python Programming Notes Made Easy: Learn by changing values and solving exercise

Requirements

• A computer: To take this course, students will need access to a computer that can run Python programming language and a code editor or Integrated Development Environment (IDE).
• Raspberry Pi (optional): Students interested in IoT applications may need a Raspberry Pi, a small computer that can be used for a variety of projects.

Description

The Ultimate Python Notes is a comprehensive course designed to help you master Python programming from beginner to advanced levels. Whether you’re starting from scratch or looking to deepen your skills, this course will provide you with everything you need to know about Python programming in a NOTABLE WAY!

This course goes beyond traditional lectures and includes practical coding exercises, projects, and coding challenges to help you gain hands-on experience. In addition to learning Python programming, you’ll also learn effective note-taking skills that will help you retain and share your knowledge with others.

The course also includes optional content related to using Raspberry Pi for IoT projects, making it ideal for students interested in IoT applications. By the end of the course, you’ll have a comprehensive set of Python programming notes that you can use for reference and share with your colleagues.

Whether you’re a student, a professional looking to improve your skills, or someone interested in the field of programming, The Ultimate Python Notes is the perfect course to take your Python programming skills to the next level.

This course is consistently updating and more notes will be available in future.
Currently, the course contains 170+ notes!

Some of the available projects are related to specific python fields such as

1. CyberSecurity
2. Data Science
3. API

Who this course is for:

• This course is ideal for anyone who wants to learn or improve their skills in Python programming. It is suitable for beginners who have no prior experience in coding, as well as intermediate or advanced learners who want to deepen their knowledge in the language.
• This course is also suitable for learners who want to improve their skills and learn how to effectively write programming codes.
• Students who are interested in IoT applications may find this course particularly valuable as it includes optional content related to using Raspberry Pi for IoT projects.
• Students who are interested in cybersecurity combined with python

Course content

8 sections • 36 lectures • 32m total length

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

What is Data Science

Who is a Data Scientist

Type of Questions that a Data Science Can Answer

Supervised and Unsupervised Learning in Machine Learning with Real life Examples

Applications of Data Science in Real Life

What is Data Engineering

Who is a Data Engineer

What is Machine Learning

Who is a Machine Learning Engineer

Skills Needed to become a Data scientist

How to Practice Data Science and Build your portfolio

Certifications in Data Science

Some Great Books in Data Science

Requirements

• Laptop or PC
• A Good Connection to the internet
• Passion to Learn about Data Science

Description

Data science and machine learning is one of the hottest fields in the market and has a bright future

In the past ten years, many courses have appeared that explains the field in a more practical way than in theory

During my experience in counseling and mentoring, I faced many obstacles, the most important of which was the existence of educational gaps for the learner, and most of the gaps were in the theoretical field.

To fill this gap, I made this course, Thank God, this course helped many students to properly understand the field of data science.

If you have no idea what the field of data science is and are looking for a very quick introduction to data science, this course will help you become familiar with and understand some of the main concepts underlying data science.

If you are an expert in the field of data science, then attending this course will give you a general overview of the field

This short course will lay a strong foundation for understanding the most important concepts taught in advanced data science courses, and this course will be very suitable if you do not have any idea about the field of data science and want to start learning data science from scratch

Who this course is for:

• Data Science Enthusiasts
• People who wants to Become Data Scientists
• Data Science Aspirants

Course content

Learn Terms used in Machine Learning in Python 312 285 6886

Learn the Basics of Model building without math or programming knowledge

Entry point to Data Science, Machine Learning Career in NYC New York

Requirements

• Python 101 (3-10 hours)
• Data Science 101 (3-10 hours)
• Career in Data Science (3-10 hours)

Description

Machine Learning 101 Class Bootcamp Course NYC

1. Python Scikit-learn Library
2. Supervised vs Unsupervised Learning
3. Regression vs Classification models
4. Categorical vs Continuous feature spaces
5. Modeling Fundamentals: Test-train split, Cross validation(CV), Bias–variance tradeoff, Precision and Recall, Ensemble models
6. Interpreting Results of Regression and  Classification Models (Hands On)
7. Parameters and Hyper Parameters
8. SVM, K-Nearest Neighbor, Neural Networks
9. Dimension Reduction

Hands on:

1. Understanding and Interpreting results of Regression and Logistic Regression using Google Spreadsheets and Python
2. Calculating R-Square, MSE, Logit manually in excel for enhanced understanding (Multiple Regression)
3. Understanding features of Popular Datasets: Titanic, Iris (Scikit) and Housing Prices
4. Running Logistic Regression on Titanic Data Set
5. Running Regression, Logistic Regression, SVM and Random Forest on Iris Dataset

Who this course is for:

• Python and Data Analytics
• Programmers with no knowledge of Maths
• New Entrants in Data Science Field

Course content

About this Course

Kickstart your learning of Python with this beginner-friendly self-paced course taught by an expert. Python is one of the most popular languages in the programming and data science world and demand for individuals who have the ability to apply Python has never been higher.

This introduction to Python course will take you from zero to programming in Python in a matter of hours—no prior programming experience necessary! You will learn about Python basics and the different data types. You will familiarize yourself with Python Data structures like List and Tuples, as well as logic concepts like conditions and branching. You will use Python libraries such as Pandas, Numpy & Beautiful Soup. You’ll also use Python to perform tasks such as data collection and web scraping with APIs. You will practice and apply what you learn through hands-on labs using Jupyter Notebooks. By the end of this course, you’ll feel comfortable creating basic programs, working with data, and automating real-world tasks using Python. This course is suitable for anyone who wants to learn Data Science, Data Analytics, Software Development, Data Engineering, AI, and DevOps as well as a number of other job roles.

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This course is part of multiple programs

This course can be applied to multiple Specializations or Professional Certificates programs. Completing this course will count towards your learning in any of the following programs:

• IBM Data Science Professional Certificate
• IBM Data Analyst Professional Certificate

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WHAT YOU WILL LEARN

• Describe Python Basics including Data Types, Expressions, Variables, and Data Structures.
• Apply Python programming logic using Branching, Loops, Functions, Objects & Classes.
• Demonstrate proficiency in using Python libraries such as Pandas, Numpy, and Beautiful Soup.
• Access web data using APIs and web scraping from Python in Jupyter Notebooks.

SKILLS YOU WILL GAIN

• Data Science
• Python Programming
• Data Analysis
• Pandas
• Numpy

About this Course

Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning.

This course will begin with a gentle introduction to Machine Learning and what it is, with topics like supervised vs unsupervised learning, linear & non-linear regression, simple regression and more. You will then dive into classification techniques using different classification algorithms, namely K-Nearest Neighbors (KNN), decision trees, and Logistic Regression. You’ll also learn about the importance and different types of clustering such as k-means, hierarchical clustering, and DBSCAN. With all the many concepts you will learn, a big emphasis will be placed on hands-on learning. You will work with Python libraries like SciPy and scikit-learn and apply your knowledge through labs. In the final project you will demonstrate your skills by building, evaluating and comparing several Machine Learning models using different algorithms. By the end of this course, you will have job ready skills to add to your resume and a certificate in machine learning to prove your competency.

SHOW ALL COURSE OUTLINESHOW ALL

This course is part of multiple programs

This course can be applied to multiple Specializations or Professional Certificates programs. Completing this course will count towards your learning in any of the following programs:

• IBM Data Science Professional Certificate
• IBM AI Engineering Professional Certificate

WHAT YOU WILL LEARN

• Describe the various types of Machine Learning algorithms and when to use them 
• Compare and contrast linear classification methods including multiclass prediction, support vector machines, and logistic regression 
• Write Python code that implements various classification techniques including K-Nearest neighbors (KNN), decision trees, and regression trees 
• Evaluate the results from simple linear, non-linear, and multiple regression on a data set using evaluation metrics 

SKILLS YOU WILL GAIN

• SciPy and scikit-learn
• Machine Learning
• regression
• classification
• Hierarchical Clustering