Tag: Jupyter Notebook

Introduction to Python, Anaconda 3 and Jupyter Notebook

Requirements

• No programming experience needed. You will learn everything you need to know

Description

Today, we are all surrounded with full of data.

Data can be in the form of structured data(eg: Tables, and worksheets), or unstructured data (free text fields or comments from social media).

Data can also be in the form of a bi-product produced during day-to-day transactions.

For example, when we are buying something from supermarkets, we are issued a resit upon payment. The resit is a bi-product as our intention is not to collect the resit but to retrieve all the goods that we purchased from the supermarket. If we take a look at the receipt, it has sufficient data as evidence that we bought the specific product from the supermarket. It has all the required data to perform a return when the product bought has defects. It has the date purchased, the location of the store, and the list of products, unit cost, and quantities purchased.

The question is:

1. How can we further increase our revenue with the data that we have?

2. How can we predict customer purchasing behavior?

3. How can we know what all the necessary products the customer would buy if they had purchased a certain product?

Data is the core of an AI model, which utilizes data input for the model to train, test, and learn from the data.

The usage of Machine Learning has allowed computers to perform predictions and provides suggestions to humans based on the data input that has been fed into the machine.

The AI Model would predict what is the next purchase of the customer, based on the data that has been fed into the model.

Join now to know more about Python as a basic step toward Data Science. 

The Objective of the course:

To provide a very understanding of the basic functionality of Python.

Learning Outcomes:

1. How to install and configure Python.

2. Python Function and Class Objects.

3. Data Types – String and Numeric.

4. Python Data Structure – List and Data Dictionary.

Python Tutorials, Anaconda 3, Jupyter Notebook, Python 3

Who this course is for:

• IT Fresh Graduates
• Data Scientist Beginners

Course content

4 sections • 22 lectures • 1h 24m 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

Understand how to take a model from notebook to deployment

Ability to use Flask framework for machine learning model deployment

Ability to Use Postman application to test your model API endpoints

Understand How to leverage on Datasist ibrary for faster model building and deployment

Requirements

• Introductory knowledge to Python suffices

Description

This course is for every beginner in the data science space. We have been there before and we understood what your learning challenges are. This short course will focus on showing you end to end what it takes to build and deploy a simple machine learning solution.

We will be building a car pricing prediction engine. This will be purely hands-on.

You will be able to deploy this solution using the flask framework as an API and also as a Platform.

We will also introduce you to libraries that make it easy to quickly explore, build, and deploy a machine learning solution.

This course assumes you do not have any prior knowledge of Machine Learning or that you have taken a couple of courses but still missing the full picture.

Machine learning models are not useful in silos and this free course will show you the full picture and cover the knowledge gaps.

You will also be able to put to use your knowledge of HTML as we build a simple web interface to interact with the solution.

This course will also introduce you to Postman Application. It is a popular use for testing API and solutions. Postman will help us to interact with the API deployment of our model while our browser is sufficient for interacting with the platform deployment.

We will be making use of the following major applications:

1. Jupyter Notebook

2. Visual Studio Code

3. Postman

Who this course is for:

• Data Science Enthusiast
• Beginner Python Developers curious about Data Science
• Data Analyst willing to move to the predictive space
• Business People curious about model building and deployment

Course content

You’ll receive the completely annotated Jupyter Notebook used in the course.

You’ll be able to define and give examples of the top libraries in Python used to build real world predictive models.

You will be able to create models with the most powerful language for machine learning there is.

You’ll understand the supervised predictive modeling process and learn the core vernacular at a high level.

Requirements

• There are no prerequisites however knowledge of Python will be helpful.
• A familiarity with the concepts of machine learning would be helpful but aren’t necessary.

Description

Recent Review from Similar Course:

“This was one of the most useful classes I have taken in a long time. Very specific, real-world examples. It covered several instances of ‘what is happening’, ‘what it means’ and ‘how you fix it’. I was impressed.”  Steve

Welcome to The Top 5 Machine Learning Libraries in Python.  This is an introductory course on the process of building supervised machine learning models and then using libraries in a computer programming language called Python.

What’s the top career in the world? Doctor? Lawyer? Teacher? Nope. None of those.

The top career in the world is the data scientist. Great. What’s a data scientist?

The area of study which involves extracting knowledge from data is called Data Science and people practicing in this field are called as Data Scientists.

Business generate a huge amount of data.  The data has tremendous value but there so much of it where do you begin to look for value that is actionable? That’s where the data scientist comes in.  The job of the data scientist is to create predictive models that can find hidden patterns in data that will give the business a competitive advantage in their space.

Don’t I need a PhD?  Nope. Some data scientists do have PhDs but it’s not a requirement.  A similar career to that of the data scientist is the machine learning engineer.

A machine learning engineer is a person who builds predictive models, scores them and then puts them into production so that others in the company can consume or use their model.  They are usually skilled programmers that have a solid background in data mining or other data related professions and they have learned predictive modeling.

In the course we are going to take a look at what machine learning engineers do. We are going to learn about the process of building supervised predictive models and build several using the most widely used programming language for machine learning. Python. There are literally hundreds of libraries we can import into Python that are machine learning related.

A library is simply a group of code that lives outside the core language. We “import it” into our work space when we need to use its functionality. We can mix and match these libraries like Lego blocks.

Thanks for your interest in the The Top 5 Machine Learning Libraries in Python and we will see you in the course. 

Who this course is for:

• If you’re looking to learn machine learning then this course is for you.

Course content

Build a report of actionable insights using modeling and data analysis

Model student behavior using XGBoost and predict struggling/at-risk students

Explore student data and identify what makes a struggling student different than successful students

Help teachers help students – and apply this insight-extracting approach to your other projects and models

Requirements

• Knowledge of Python and the basics of modeling
• Ability to run a Jupyter Notebook and install appropriate Python libraries

Description

Applied data science is about everything that goes before and after your model. Extracting actionable insights is probably the most important aspect of any modeling project! if you want to step up your data science game then this is a great area to study. Let’s do it hands-on, applied a science project together and walk through a student retention model to extract actionable insights and help out struggling students.

• Explore student data
• Model student behavior using XGBoost
• Predict struggling/at-risk students
• Identify what makes a struggling student different than successful students
• Build a report of actionable insights
• And help teachers help students

In the case of a student retention model, looking at the full picture means doing a lot of work before doing any modeling. For example, talking to teachers. We need to better understand the business domain. In this case, finding out what are the problems they face. What are the uncertainties they’d like help with? It is critical to also leverage all their knowledge, like how and when do they determine that a student is at-risk. What data points and triggers do they use to identify someone that could be failing a class and/or their studies. How early can they identify this? Obviously the earlier the better, you don’t want to wait till have too many bad grades and can’t dig themselves out of the hole.

After you’ve distilled all that information in the model, we dig down into the observation level. This is an important point to understand. A model may return feature importance, coefficients, or weights depending on what type of model you use and how it learns. So, imagine a model that predicts heart attacks and finds that older age is the most important feature for the model, and if your patient is young, that’s not going to tell them anything, worse, may lead them to misdiagnose.

Instead, we let the model give us a prediction of the likelihood of something happening, then we dig down to the observation level (i.e. each specific patient or student level) where each case is different and unique and analyze what makes this particular patient/student different from the rest. This may yield some useful information that may allow the professional to better assist – that is actionable insight.

Who this course is for:

• Those interested in stepping up their practical machine learning and analytics knowledge
• Those interested in getting more out of their machine learning projects

Course content