Machine Learning Tutorial | Learn Machine Learning 12 hours | Intellipaat

Machine Learning Tutorial | Learn Machine Learning 12 hours | Intellipaat

Learn machine learning 12 hours in this machine learning tutorial. 🔥🔥Intellipaat Machine Learning course: https://intellipaat.com/machine-learning-certificat...

This machine learning tutorial covers what is machine learning, machine learning algorithms like linear regression, binary classification, decision tree, random forest and unsupervised algorithm like k means clustering in detail with complete hands on demo. There is machine learning complete project and machine learning interview questions as well in this machine learning full course video to prepare you for the job interview.

Why should you watch this machine learning tutorial?

Machine learning is one of the fastest growing arms of the domain of artificial intelligence. It has far reaching consequences and in the next couple of years we will be seeing every industry deploying the principles of artificial intelligence, machine learning and deep learning technologies at scale.

Who should watch this machine learning tutorial video?

This machine learning tutorial is for everybody right from professionals in analytics, data science domains, eCommerce, or in search engine domains. If you are a Software professionals looking for a career switch and fresh graduates then also you can watch this tutorial.

Why machine learning is important?

Machine learning might just be one of the most important fields of science that we are just moving towards. It differs from other science in the sense that this is one of the one domains where the input and output are not directly correlated and neither do we provide the input for every task that the machine will perform. It is more about mimicking how humans think and solving real world problems like humans without actually the intervention of humans. It focuses on developing computer programs that can be taught to grown and change when exposed to data.

Machine Learning Tutorial | Learn Machine Learning 12 hours | Intellipaat

Machine Learning Tutorial | Learn Machine Learning 12 hours | Intellipaat

Learn machine learning 12 hours in this machine learning tutorial. 🔥🔥Intellipaat Machine Learning course: https://intellipaat.com/machine-learning-certificat...

This machine learning tutorial covers what is machine learning, machine learning algorithms like linear regression, binary classification, decision tree, random forest and unsupervised algorithm like k means clustering in detail with complete hands on demo. There is machine learning complete project and machine learning interview questions as well in this machine learning full course video to prepare you for the job interview.
Why should you watch this machine learning tutorial?

Machine learning is one of the fastest growing arms of the domain of artificial intelligence. It has far reaching consequences and in the next couple of years we will be seeing every industry deploying the principles of artificial intelligence, machine learning and deep learning technologies at scale.

Who should watch this machine learning tutorial video?

This machine learning tutorial is for everybody right from professionals in analytics, data science domains, eCommerce, or in search engine domains. If you are a Software professionals looking for a career switch and fresh graduates then also you can watch this tutorial.

Why machine learning is important?

Machine learning might just be one of the most important fields of science that we are just moving towards. It differs from other science in the sense that this is one of the one domains where the input and output are not directly correlated and neither do we provide the input for every task that the machine will perform. It is more about mimicking how humans think and solving real world problems like humans without actually the intervention of humans. It focuses on developing computer programs that can be taught to grown and change when exposed to data.

5 Python Online Courses for Beginners

5 Python Online Courses for Beginners

If you are thinking to learn a new programming language then also Python is a good choice, particularly if you are looking to move towards a lucrative career path of Data Science and Machine learning which has lots of opportunities. In this article, I am going to share some of the best online courses to learn Python in 2020...

Python is an object-oriented, high-level programming language with integrated dynamic semantics primarily for web and app development. It is extremely attractive in the field of Rapid Application Development because it offers dynamic typing and dynamic binding options.

Python is relatively simple, so it's easy to learn since it requires a unique syntax that focuses on readability. Developers can read and translate Python code much easier than other languages. In turn, this reduces the cost of program maintenance and development because it allows teams to work collaboratively without significant language and experience barriers.

Additionally, Python supports the use of modules and packages, which means that programs can be designed in a modular style and code can be reused across a variety of projects. Once you've developed a module or package you need, it can be scaled for use in other projects, and it's easy to import or export these modules.

In recent years, Python has also become a default language for Data Science and Machine learning Projects and that's another reason why many experienced programmers are learning Python .

If you are thinking to learn a new programming language then also Python is a good choice, particularly if you are looking to move towards a lucrative career path of Data Science and Machine learning which has lots of opportunities. In this article, you will find free online courses in python programming, but not only will you find one, but you will also find 5 more courses on Python! I am going to share some of the best online courses to learn Python in 2020

They are high quality courses with more than 4 star rating (from 0 to 5 stars), that means if you are starting your career with the python programming language, these are the best courses that will take you step-by-step , to start and learn from scratch the fundamentals about this language that so professional and useful has been in recent years.

Top 5 Courses to Learn Python in 2020

1. Complete Python Bootcamp: Go from zero to hero in Python

This is one of the most popular course to learn Python on Udemy and more than 250,000 students have enrolled in it. That speaks volumes for the quality of the course.

This is a comprehensive but straight-forward course to learn the Python programming language on Udemy! and useful for all levels of programmers.

In this course, you will learn Python 3 in a practical manner. You will start by downloading and setting up Python on your machine and then slowly move on to different topics.

It's also a practical course where an instructor will show you live coding and explain what he does.

The course also comes with quizzes, notes and homework assignments as well as 3 major projects to create a Python project portfolio! which complements your learning.

2. 30 Days of Python | Unlock your Python Potential

In early 2016, Python passed Java as the #1 beginners language in the world. Why? It's because it's simple enough for beginners yet advanced enough for the pros.

You can not only write simple scripts to automate stuff but also create a complex program to handle trades. You can even use Python for it for IOT, Web Development, Big Data, Data Science, Machine learning and more.

This is a very practical course and useful not just for beginners but also for programmers who know other programming languages e.g. Java, C++ and want to learn Python.

In 30 days this course will teach you to write complex Python applications to scrape Data from nearly any website and Build your own Python applications for all types of automation. It's perfect for busy developers who learn by doing serious stuff.

3. Python for Beginners with Examples


This online Python course is taught by Ardit Sulce ,This Python course has everything you need to know to start coding in Python and not even that, by the end of the course you will know how to build complete programs and also build graphical user interfaces for your programs so you can impress your employer or your friends. This course will guide you step by step starting from the basics and always assuming you don't have previous programming experience or a computer science degree. In fact, most people who learn Python come from a vast variety of careers.

This course has all you need to get you started. After you take it you will be ready to go to the next level of specializing in any of the Python paths such as data science or web development. Python is one of the most needed skills nowadays. Sign up today!

4. Learn Python Programming Masterclass


This is another fantastic course to learn Python on Udemy. This course is taught by Tim Buchalka,I am a big fan of Tim Buchalka and have attended a couple of his courses.

This course is aimed at complete beginners who have never programmed before, as well as existing programmers who want to increase their career options by learning Python.

The fact is, Python is one of the most popular programming languages in the world – Huge companies like Google use it in mission critical applications like Google Search.

And Python is the number one language choice for machine learning, data science and artificial intelligence. To get those high paying jobs you need an expert knowledge of Python, and that’s what you will get from this course.

By the end of the course you’ll be able to apply in confidence for Python programming jobs. And yes, this applies even if you have never programmed before. With the right skills which you will learn in this course, you can become employable and valuable in the eyes of future employers.

5. The Python Bible™ | Everything You Need to Program in Python

This course was developed by Ziyad Yehia , a renowned instructor on Udemy. Currently, This course has nearly 78,000 students and excellent star ratings.

This is a project-based course and you will build 11 Projects int this Python Course.

If you enjoy hands-on learning while working on the project rather than learning individual concept then this course is for you.

This is a comprehensive, in-depth and meticulously prepared course and teaches you everything you need to know to program in Python. It delivers what is promised in the title, A-Z, it's all here!

Conclusion

That's all about the best courses to learn Python in depth. you can begin with these courses, don't need to buy all of them, just choose the one where you can connect with instructor.

These courses will give you a solid foundation and confidence to use Python in your project.

==========================================================

Thanks for reading

Build Docker Container with Machine Learning Model for Beginners

Build Docker Container with Machine Learning Model for Beginners

A Complete Guide with Template Scripts for Docker Beginners

As a data scientist, I don’t have a lot of software engineering experience but I have certainly heard a lot of great comments about containers. I have heard about how lightweight they are compared to traditional VMs and how good they are at ensuring a safe consistent environment for your code.

However, when I tried to Dockerize my own model, I soon realized it is not that intuitive. It is not at all as simple as putting RUN in front of your EC2 bootstrap script. I found that inconsistencies and unpredictable behaviors happen quite a lot and it can be frustrating to learn to debug a new tool.

All of these motivated me to create this post with all the code snippets you need to factorize your ML model in Python to a Docker container. I will guide you through installing all the pip packages you need and build your first container image. And in the second part of this post, we will be setting up all the necessary AWS environment and kicking off the container as a Batch job.

Disclaimer: The model I am talking about here is a batch job on a single instance, NOT a web service with API endpoints, NOT distributed parallel jobs. If you follow this tutorial, the whole process to put your code to a container should not take more than 25 minutes.

Prerequisite
  • an AWS account

  • AWS CLI installed

  • Docker installed, and username setup

  • Python 3 installed

Step 1 Create a Dockerfile

To get your code to a container, you need to create a Dockerfile, which tells Docker what you need in your application.

FROM python:3.6-stretch
MAINTAINER Tina Bu <[email protected]>

# install build utilities
RUN apt-get update && \
	apt-get install -y gcc make apt-transport-https ca-certificates build-essential

# check our python environment
RUN python3 --version
RUN pip3 --version

# set the working directory for containers
WORKDIR  /usr/src/<app-name>

# Installing python dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy all the files from the project’s root to the working directory
COPY src/ /src/
RUN ls -la /src/*

# Running Python Application
CMD ["python3", "/src/main.py"]

minimal Dockerfile for a Python application

In the Dockerfile above, I started with the base Python 3.6 stretch image, apt-get updated the system libraries, installed some make and build stuff, checked my python and pip version to make sure they are good, set up my work directory, copied requirements.txt to the container and pip installed all the libraries in it, and finally copied all the other code files to the container, listed all the files to make sure all I need is there and triggered my entrypoint main.py file.

This Dockerfile should work for you if your code folder structure is like this.

- app-name
     |-- src
          |-- main.py
          |-- other_module.py
     |-- requirements.txt
     |-- Dockerfile
		 

All you need to do is to change the to your application name and we are ready to build an image from it.

There are a lot of best practices to make a docker file smaller and more efficient but most of them are out of the scope for this post. However, a few things you may want to be mindful about are:

1.1 Use Python stretch as Base Image

People say instead of starting with a generic Ubuntu image, use an official base image like Alpine Python instead. But I have found it extremely difficult to work with especially for installing packages (Docker experts please do teach me how it should be done properly in the comments below but while I am on my own here, I am not going to waste more time fixing Numpy installation error). A Ubuntu base image will provide predictable behavior but I suggest you start with Python 3.6 stretch, which is the official Python image based on Debian 9 (aka stretch). Python stretch comes with the Python environment and pip installed and up to date, all of which you need to figure out how to install if you choose Ubuntu.

1.2 Install Only What You Need

It’s also very tempting to copy-paste some Dockerfile template especially if this is your first Docker project. But it’s suggested to only install the things you actually need to control the size of the image. If you see a whole bunch of make and build stuff other people installed, try to not include them first and see if your container will work. A smaller image generally means it’s faster to build and deploy. (Another reason you should try my minimalism template above!)

Also to keep the image as lean as possible, use.dockerignore which works exactly like .gitignore to ignore files that won’t impact the model.

.git
.gitignore
README.md
LICENSE
Dockerfile*
docker-compose*
data/*
test/*

1.3 Add requirements.txt Before Code

In your Dockerfile, always add your requirements.txt file before copying the source code. That way, when you change your code and re-build the container, Docker will re-use the cached layer up until the installed packages instead of executing thepip install command on every build even if the packages needed never changed. No one wants to wait 1 extra minute just because you added an empty line in your code.

If you are interested to learn more about Dockerfile, in the appendix there is a quick summary of the few basic commands we used. Feel free to jump to Step 2 for building a container with the Dockerfile you just created.

Step 2 — Build an Image with your Dockerfile

docker build creates an image according to the instructions given in the Dockerfile. All you need to do is to give your image a name.

docker build -t ${IMAGE_NAME}:${VERSION} .

Check that your image exists locally with:

docker images

You can also choose to tag your image with a human-friendly name instead of using the hash ID.

docker tag ${IMAGE_ID} ${IMAGE_NAME}:${TAG}
# or
docker tag ${IMAGE_NAME}:${VERSION} ${IMAGE_NAME}:${TAG}

Now you should test your container locally to make sure everything works fine.

docker run ${IMAGE_NAME}:${TAG}

Congratulation! You just baked your model into a container that can be run anywhere Docker is installed. Join me for the second part of this post to learn how to schedule your container as a Batch job!

Appendix — Dockerfile Commands
  • FROM starts the Dockerfile. It is a requirement that the Dockerfile must start with the FROM command. Images are created in layers, which means you can use another image as the base image for your own. The FROM command defines your base layer. As arguments, it takes the name of the image. Optionally, you can add the Docker Cloud username of the maintainer and image version, in the format username/imagename:version.

  • RUN is used to build up the image you’re creating. For each RUN command, Docker will run the command then create a new layer of the image. This way you can roll back your image to previous states easily. The syntax for a RUN instruction is to place the full text of the shell command after the RUN(e.g., RUN mkdir /user/local/foo). This will automatically run in a /bin/sh shell. You can define a different shell like this: RUN /bin/bash -c 'mkdir /user/local/foo'

  • COPY copies local files into the container.

  • CMD defines the commands that will run on the Image at start-up. Unlike a RUN, this does not create a new layer for the Image, but simply runs the command. There can only be one CMD per a Dockerfile/Image. If you need to run multiple commands, the best way to do that is to have the CMD run a script. CMD requires that you tell it where to run the command, unlike RUN. So example CMD commands would be:

  • EXPOSE creates a hint for users of an image which ports provide services. It is included in the information which can be retrieved via docker inspect <container-id>.

  • Note: The EXPOSE command does not actually make any ports accessible to the host! Instead, this requires publishing ports by means of the -p flag when using docker run.

  • PUSH pushes your image to a private or cloud registry.

Thanks for reading !