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Complete Machine Learning & Data Science with Python | A-Z
Last updated 11/2023
Duration: 8h42m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 2.5 GB
Genre: eLearning | Language: English

Use Scikit, learn NumPy, Pandas, Matplotlib, Seaborn and dive into machine learning A-Z with Python and Data Science.

What you'll learn
Machine learning isn't just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries.
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 matplotlib and seaborn for data visualizations
Machine Learning with SciKit Learn
Linear Regression Algorithm
Logistic Regresion Algorithm
K Nearest Neighbors Algorithm
Decision Trees And Random Forest Algorithm
Support Vector Machine Algorithm
Unsupervised Learning
K Means Clustering Algorithm
Hierarchical Clustering Algorithm
Principal Component Analysis (PCA)
Recommender System Algorithm
Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective.
Python is a general-purpose, object-oriented, high-level programming language.
Python is a multi-paradigm language, which means that it supports many programming approaches. Along with procedural and functional programming styles
Python is a widely used, general-purpose programming language, but it has some limitations. Because Python is an interpreted, dynamically typed language
Python is a general programming language used widely across many industries and platforms. One common use of Python is scripting, which means automating tasks.
Python is a popular language that is used across many industries and in many programming disciplines. DevOps engineers use Python to script website.
Python has a simple syntax that makes it an excellent programming language for a beginner to learn. To learn Python on your own, you first must become familiar
Machine learning describes systems that make predictions using a model trained on real-world data.
Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing.
It's possible to use machine learning without coding, but building new systems generally requires code.
Python is the most used language in machine learning. Engineers writing machine learning systems often use Jupyter Notebooks and Python together.
Machine learning is generally divided between supervised machine learning and unsupervised machine learning. In supervised machine learning.
Machine learning is one of the fastest-growing and popular computer science careers today. Constantly growing and evolving.
Machine learning is a smaller subset of the broader spectrum of artificial intelligence. While artificial intelligence describes any "intelligent machine"
A machine learning engineer will need to be an extremely competent programmer with in-depth knowledge of computer science, mathematics, data science.
Python machine learning, complete machine learning, machine learning a-z

Requirements
Basic knowledge of Python Programming Language
Be Able To Operate & Install Software On A Computer
Free software and tools used during the machine learning a-z course
Determination to learn machine learning and patience.
Motivation to learn the the second largest number of job postings relative program language among all others
Data visualization libraries in python such as seaborn, matplotlib
Curiosity for machine learning python
Desire to learn Python
Desire to work on python machine learning
Desire to learn matplotlib
Desire to learn pandas
Desire to learn numpy
Desire to work on seaborn
Desire to learn machine learning a-z, complete machine learning
Description
Hello there,
Welcome to the
"Complete Machine Learning & Data Science with Python | A-Z"
course.
Use Scikit, learn NumPy, Pandas, Matplotlib, Seaborn, and dive into machine learning A-Z with Python and Data Science.
Machine learning isn't just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and new problems. Whether you're a marketer, video game designer, or programmer, my course on OAK Academy here to help you apply machine learning to your work. Complete machine learning & data science with python | a-z, machine learning a-z, Complete machine learning & data science with python, complete machine learning and data science with python a-z, machine learning using python, complete machine learning and data science, machine learning, complete machine learning, data science
It's hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazon's Alexa and the iPhone's Siri, are all technologies that function based on machine learning algorithms and mathematical models. Python, machine learning, django, python programming, machine learning python, python for beginners, data science
Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels.
Whether you work in machine learning or finance, or are pursuing a career in web development or data science, Python is one of the most important skills you can learn. Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks.
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
"Complete Machine Learning & Data Science with Python | A-Z"
a straightforward 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 a-z
world and will move deeper. You will learn the fundamentals of
Machine Learning A-Z
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 python algorithms.
This Machine Learning course is for everyone!
My "
Machine Learning with Hands-On Examples in Data Science
" 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
?
More About Machine Learning
Machine Learning Terminology
Evaluation Metrics
What is Classification vs Regression?
Evaluating Performance-Classification Error Metrics
Evaluating Performance-Regression Error Metrics
Machine Learning with Python
Supervised Learning
Cross-Validation and Bias Variance Trade-Off
Use Matplotlib and seaborn for data visualizations
Machine Learning with SciKit Learn
Linear Regression Theory
Logistic Regression Theory
Logistic Regression with Python
K Nearest Neighbors Algorithm Theory
K Nearest Neighbors Algorithm With Python
K Nearest Neighbors Algorithm Project Overview
K Nearest Neighbors Algorithm Project Solutions
Decision Trees And Random Forest Algorithm Theory
Decision Trees And Random Forest Algorithm With Python
Decision Trees And Random Forest Algorithm Project Overview
Decision Trees And Random Forest Algorithm Project Solutions
Support Vector Machines Algorithm Theory
Support Vector Machines Algorithm With Python
Support Vector Machines Algorithm Project Overview
Support Vector Machines Algorithm Project Solutions
Unsupervised Learning Overview
K Means Clustering Algorithm Theory
K Means Clustering Algorithm With Python
K Means Clustering Algorithm Project Overview
K Means Clustering Algorithm Project Solutions
Hierarchical Clustering Algorithm Theory
Hierarchical Clustering Algorithm With Python
Principal Component Analysis (PCA) Theory
Principal Component Analysis (PCA) With Python
Recommender System Algorithm Theory
Recommender System Algorithm With Python
Complete machine learning
Python machine learning
Machine learning a-z
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.
What is machine learning?
Machine learning
describes systems that make predictions using a model trained on real-world data. For example, let's say we want to build a system that can identify if a cat is in a picture. We first assemble many pictures to train our machine learning model. During this training phase, we feed pictures into the model, along with information around whether they contain a cat. While training, the model learns patterns in the images that are the most closely associated with cats. This model can then use the patterns learned during training to predict whether the new images that it's fed contain a cat. In this particular example, we might use a neural network to learn these patterns, but machine learning can be much simpler than that. Even fitting a line to a set of observed data points, and using that line to make new predictions, counts as a machine learning model.
What is machine learning used for?
Machine learning a-z
is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing, and more. In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use. Machine learning is often a disruptive technology when applied to new industries and niches. Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes. With the right data, you can use
machine learning
technology to identify extremely complex patterns and yield highly accurate predictions.
Does Machine learning require coding?
It's possible to use
machine learning data science
without coding, but building new systems generally requires code. For example, Amazon's Rekognition service allows you to upload an image via a web browser, which then identifies objects in the image. This uses a pre-trained model, with no coding required. However, developing machine learning systems involves writing some
Python
code to train, tune, and deploy your models. It's hard to avoid writing code to pre-process the data feeding into your model. Most of the work done by a machine learning practitioner involves cleaning the data used to train the machine. They also perform "feature engineering" to find what data to use and how to prepare it for use in a machine learning model. Tools like AutoML and SageMaker automate the tuning of models. Often only a few lines of code can train a model and make predictions from it
What is the best language for machine learning?
Python is the most used language in
machine learning using python
. Engineers writing machine learning systems often use Jupyter Notebooks and Python together. Jupyter Notebooks is a web application that allows experimentation by creating and sharing documents that contain live code, equations, and more. Machine learning involves trial and error to see which hyperparameters and feature engineering choices work best. It's useful to have a development environment such as Python so that you don't need to compile and package code before running it each time. Python is not the only language choice for machine learning. Tensorflow is a popular framework for developing neural networks and offers a C++ API. There is a
complete machine learning
framework for C# called ML. NET. Scala or Java are sometimes used with Apache Spark to build machine learning systems that ingest massive data sets.
What are the different types of machine learning?
Machine learning is generally divided between supervised machine learning and unsupervised machine learning. In supervised machine learning, we train machine learning models on labeled data. For example, an algorithm meant to detect spam might ingest thousands of email addresses labeled 'spam' or 'not spam.' That trained model could then identify new spam emails even from data it's never seen. In unsupervised learning, a machine learning model looks for patterns in unstructured data. One type of unsupervised learning is clustering. In this example, a model could identify similar movies by studying their scripts or cast, then group the movies together into genres. This unsupervised model was not trained to know which genre a movie belongs to. Rather, it learned the genres by studying the attributes of the movies themselves. There are many techniques available within.
Is Machine learning a good career?
Machine learning python
is one of the fastest-growing and popular computer science careers today. Constantly growing and evolving, you can apply machine learning to a variety of industries, from shipping and fulfillment to medical sciences. Machine learning engineers work to create artificial intelligence that can better identify patterns and solve problems. The machine learning discipline frequently deals with cutting-edge, disruptive technologies. However, because it has become a popular career choice, it can also be competitive. Aspiring machine learning engineers can differentiate themselves from the competition through certifications, boot camps, code repository submissions, and hands-on experience.
What is the difference between machine learning and artifical intelligence?
Machine learning is a smaller subset of the broader spectrum of artificial intelligence. While artificial intelligence describes any "intelligent machine" that can derive information and make decisions, machine learning descr

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