Machine Learning with Hands-On Examples

[mitsumi]

Machine Learning with Hands-On Examples
MP4 | h264, 1280x720 | Lang: English | Audio: aac, 44100 Hz | 3h 51m | 1.36 GB
Use Scikit, learn NumPy, Pandas, MatDescriptionlib, Seaborn and dive in Machine Learning with Python real life exercises

What you'll learn
Learn Machine Learning with Hands-On Examples
What is Machine Learning?
Machine Learning Terminology
Evaluation Metrics
What are Classification vs Regression?
Evaluating Performance-Classification Error Metrics
Evaluating Performance-Regression Error Metrics
Supervised Learning
Cross Validation and Bias Variance Trade-Off
Use matDescriptionlib and seaborn for data visualizations
Machine Learning with SciKit Learn
Linear Regression
Logistic Regresion

Requirements
Basic knowledge of Python Programming Language
Be Able To Operate & Install Software On A Computer
Free software and tools used during the course
Determination to learn and patience.
Motivation to learn the the second largest number of job postings relative program language among all others
Description
Hello there,

Welcome to the "Machine Learning with Hands-On Examples" course.

Do you know data science needs will create 11.5 million job openings by 2026?

Do you know the average salary is $100.000 for data science careers!

Data Science Careers Are Shaping The Future

Data science experts are needed in almost every field, from government security to dating apps. Millions of businesses and government departments rely on big data to succeed and better serve their customers. So data science careers are in high demand.

If you want to learn one of the employer's most request skills?

If you are curious about Data Science and looking to start your self-learning journey into the world of data with Python?

If you are an experienced developer and looking for a landing in Data Science!

In all cases, you are at the right place!

We've designed for you "Machine Learning with Hands-On Examples" a straight-forward course for Python Programming Language and Machine Learning.

In the course, you will have down-to-earth way explanations with projects. With this course, you will learn Machine Learning step-by-step. I made it simple and easy with exercises, challenges, and lots of real-life examples.

We will open the door of the Data Science and Machine Learning world and will move deeper. You will learn the fundamentals of Machine Learning and its beautiful libraries such as Scikit Learn.

Throughout the course, we will teach you how to use Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms.

This Machine Learning course is for everyone!

My "Machine Learning with Hands-On Examples" is for everyone! If you don't have any previous experience, not a problem! This course is expertly designed to teach everyone from complete beginners, right through to professionals ( as a refresher).

Why we use a Python programming language in Machine learning?

Python is a general-purpose, high-level, and multi-purpose programming language. The best thing about Python is, it supports a lot of today's technology including vast libraries for Twitter, data mining, scientific calculations, designing, back-end server for websites, engineering simulations, artificial learning, augmented reality and what                                                                                                                                                                                                       not! Also, it supports all kinds of App development.

What you will learn?

In this course, we will start from the very beginning and go all the way to the end of "Machine Learning" with examples.

Before each lesson, there will be a theory part. After learning the theory parts, we will reinforce the subject with practical examples.

During the course you will learn the following topics:

What is Machine Learning?

Machine Learning Terminology

Evaluation Metrics

What is Classification vs Regression?

Evaluating Performance-Classification Error Metrics

Evaluating Performance-Regression Error Metrics

Supervised Learning

Cross-Validation and Bias Variance Trade-Off

Use MatDescriptionlib and seaborn for data visualizations

Machine Learning with SciKit Learn

Linear Regression

Logistic Regression

With my up-to-date course, you will have a chance to keep yourself up-to-date and equip yourself with a range of Python programming skills. I am also happy to tell you that I will be constantly available to support your learning and answer questions.

Why would you want to take this course?

Our answer is simple: The quality of teaching.

When you enroll, you will feel the OAK Academy`s seasoned developers' expertise.

Video and Audio Production Quality

All our videos are created/produced as high-quality video and audio to provide you the best learning experience.

You will be,

Seeing clearly

Hearing clearly

Moving through the course without distractions

You'll also get:

Lifetime Access to The Course

Fast & Friendly Support in the Q&A section

Udemy Certificate of Completion Ready for Download

We offer full support, answering any questions.

If you are ready to learn Machine Learning with Hands-On Examples

Dive in now! See you in the course!

Who this course is for:
Anyone who wants to start learning "Machine Learning"
Anyone who needs a complete guide on how to start and continue their career with machine learning
Software developer who wants to learn "Machine Learning"
Students Interested in Beginning Data Science Applications in Python Environment
People Wanting to Specialize in Anaconda Python Environment for Data Science and Scientific Computing
Students Wanting to Learn the Application of Supervised Learning (Classification) on Real Data Using Python

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