Machine Learning and Deep Learning A-Z: Hands-On Python

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Welcome to the “Machine Learning and Deep Learning A-Z: Hands-On Python ” course.
Python Machine Learning and Python Deep Algorithms in Python Code templates included. Python in Data Science | 2021

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!

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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 Udemy here to help you apply machine learning to your work.

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.

Udemy offers highly-rated data science courses that will help you learn how to visualize and respond to new data, as well as develop innovative new technologies. Whether you’re interested in machine learning, data mining, or data analysis, Udemy has a course for you.

• 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 and Deep Learning A-Z: Hands-On Python ” 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.

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.

Because data can mean an endless number of things, it’s important to choose the right visualization tools for the job. Whether you’re interested in learning Tableau, D3.js, After Effects, or Python, Udemy has a course for you.

In this course, we will learn what is data visualization and how does it work with python.

This course has suitable for everybody who is interested data vizualisation concept.

First of all, in this course, we will learn some fundamentals of pyhton, and object-oriented programming ( OOP ). These are our first steps in our Data Visualisation journey. After then we take a our journey to the Data Science world. Here we will take a look at data literacy and data science concepts. Then we will arrive at our next stop. Numpy library. Here we learn what is NumPy and how we can use it. After then we arrive at our next stop. Pandas library. And now our journey becomes an adventure. In this adventure we'll enter the Matplotlib world then we exit the Seaborn world. Then we'll try to understand how we can visualize our data, data viz. But our journey won’t be over. Then we will arrive at our final destination. Geographical drawing or best known as Geoplotlib in tableau data visualization.

Learn python and how to use it to python data analysis and visualization, present data. Includes tons of code data vizualisation.

In this course, you will learn data analysis and visualization in detail.

Also during the course, you will learn:

The Logic of Matplotlib

• What is Matplotlib

• Using Matplotlib

• Pyplot – Pylab - Matplotlib - Excel

• Figure, Subplot, Multiplot, Axes,

• Figure Customization

• Plot Customization

• Grid, Spines, Ticks

• Basic Plots in Matplotlib

• Overview of Jupyter Notebook and Google Colab

1. Seaborn library with these topics

   • What is Seaborn

   • Controlling Figure Aesthetics

   • Color Palettes

   • Basic Plots in Seaborn

   • Multi-Plots in Seaborn

   • Regression Plots and Squarify

2. Geoplotlib with these topics

   • What is Geoplotlib

   • Tile Providers and Custom Layers

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

• Machine learning

• Machine learning python

• Ethical hacking, python Bootcamp

• Data analysis

• Python machine learning

• Python programming

• Python examples

• Python hands-on

• Deep learning a-z

• Machine learning a-z

• Machine learning & data science a-z

• machine learning algorithms

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.


This course has suitable for everybody who interested in Machine Learning and Deep Learning concepts.

First of all, in this course, we will learn some fundamental stuff of Python and the Numpy library. These are our first steps in our Deep Learning journey. After then we take a little trip to Machine Learning history. Then we will arrive at our next stop. Machine Learning. Here we learn the machine learning concepts, machine learning workflow, models and algorithms, and what is neural network concept. After then we arrive at our next stop. Artificial Neural network. And now our journey becomes an adventure. In this adventure we'll enter the Keras world then we exit the Tensorflow world. Then we'll try to understand the Convolutional Neural Network concept. But our journey won't be over. Then we will arrive at Recurrent Neural Network and LTSM. We'll take a look at them. After a while, we'll trip to the Transfer Learning concept. And then we arrive at our final destination. Projects. Our play garden. Here we'll make some interesting machine learning models with the information we've learned along our journey.

During the course you will learn:

What is the AI, Machine Learning, and Deep Learning

1. History of Machine Learning

2. Turing Machine and Turing Test

3. The Logic of Machine Learning such as

   • Understanding the machine learning models

   • Machine Learning models and algorithms

   • Gathering data

   • Data pre-processing

   • Choosing the right algorithm and model

   • Training and testing the model

   • Evaluation

4. Artificial Neural Network with these topics

   • What is ANN

   • Anatomy of NN

   • Tensor Operations

   • The Engine of NN

   • Keras

   • Tensorflow

5. Convolutional Neural Network

6. Recurrent Neural Network and LTSM

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

Before we start this course, we will learn which environments we can be used for developing deep learning projects.

Why would you want to take this course?

Our answer is simple: The quality of teaching.

OAK Academy based in London is an online education company. OAK Academy gives education in the field of IT, Software, Design, development in English, Portuguese, Spanish, Turkish, and a lot of different languages on the Udemy platform where it has over 1000 hours of video education lessons. OAK Academy both increases its education series number by publishing new courses, and it makes students aware of all the innovations of already published courses by upgrading.

When you enroll, you will feel the OAK Academy`s seasoned developers' expertise. Questions sent by students to our instructors are answered by our instructors within 48 hours at the latest.

What are the limitations of Python?

Python is a widely used, general-purpose programming language, but it has some limitations. Because Python is an interpreted, dynamically typed language, it is slow compared to a compiled, statically typed language like C. Therefore, Python is useful when speed is not that important. Python's dynamic type system also makes it use more memory than some other programming languages, so it is not suited to memory-intensive applications. The Python virtual engine that runs Python code runs single-threaded, making concurrency another limitation of the programming language. Though Python is popular for some types of game development, its higher memory and CPU usage limits its usage for high-quality 3D game development. That being said, computer hardware is getting better and better, and the speed and memory limitations of Python are getting less and less relevant making Python even more popular.

How is Python used?

Python is a general programming language used widely across many industries and platforms. One common use of Python is scripting, which means automating tasks in the background. Many of the scripts that ship with Linux operating systems are Python scripts. Python is also a popular language for machine learning, data analytics, data visualization, and data science because its simple syntax makes it easy to quickly build real applications. You can use Python to create desktop applications. Many developers use it to write Linux desktop applications, and it is also an excellent choice for web and game development. Python web frameworks like Flask and Django are a popular choice for developing web applications. Recently, Python is also being used as a language for mobile development via the Kivy third-party library, although there are currently some drawbacks Python needs to overcome when it comes to mobile development.

What jobs use Python?

Python is a popular language that is used across many industries and in many programming disciplines. DevOps engineers use Python to script website and server deployments. Web developers use Python to build web applications, usually with one of Python's popular web frameworks like Flask or Django. Data scientists and data analysts use Python to build machine learning models, generate data visualizations, and analyze big data. Financial advisors and quants (quantitative analysts) use Python to predict the market and manage money. Data journalists use Python to sort through information and create stories. Machine learning engineers use Python to develop neural networks and artificial intelligent systems.

How do I learn Python on my own?

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 with the syntax. But you only need to know a little bit about Python syntax to get started writing real code; you will pick up the rest as you go. Depending on the purpose of using it, you can then find a good Python tutorial, book, or course that will teach you the programming language by building a complete application that fits your goals. If you want to develop games, then learn Python game development. If you're going to build web applications, you can find many courses that can teach you that, too. Udemy’s online courses are a great place to start if you want to learn Python on your own.

What is data science?

We have more data than ever before. But data alone cannot tell us much about the world around us. We need to interpret the information and discover hidden patterns. This is where data science comes in. Data science uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction. Data science seeks to find patterns in data and use those patterns to predict future data. It draws on machine learning to process large amounts of data, discover patterns, and predict trends. Data science includes preparing, analyzing, and processing data. It draws from many scientific fields, and as a science, it progresses by creating new algorithms to analyze data and validate current methods.

What does a data scientist do?

Data Scientists use machine learning to discover hidden patterns in large amounts of raw data to shed light on real problems. This requires several steps. First, they must identify a suitable problem. Next, they determine what data are needed to solve such a situation and figure out how to get the data. Once they obtain the data, they need to clean the data. The data may not be formatted correctly, it might have additional unnecessary data, it might be missing entries, or some data might be incorrect. Data Scientists must, therefore, make sure the data is clean before they analyze the data. To analyze the data, they use machine learning techniques to build models. Once they create a model, they test, refine, and finally put it into production.

What are the most popular coding languages for data science?

Python is the most popular programming language for data science. It is a universal language that has a lot of libraries available. It is also a good beginner language. R is also popular; however, it is more complex and designed for statistical analysis. It might be a good choice if you want to specialize in statistical analysis. You will want to know either Python or R and SQL. SQL is a query language designed for relational databases. Data scientists deal with large amounts of data, and they store a lot of that data in relational databases. Those are the three most-used programming languages. Other languages such as Java, C++, JavaScript, and Scala are also used, albeit less so. If you already have a background in those languages, you can explore the tools available in those languages. However, if you already know another programming language, you will likely be able to pick up Python very quickly.

How long does it take to become a data scientist?

This answer, of course, varies. The more time you devote to learning new skills, the faster you will learn. It will also depend on your starting place. If you already have a strong base in mathematics and statistics, you will have less to learn. If you have no background in statistics or advanced mathematics, you can still become a data scientist; it will just take a bit longer. Data science requires lifelong learning, so you will never really finish learning. A better question might be, "How can I gauge whether I know enough to become a data scientist?" Challenge yourself to complete data science projects using open data. The more you practice, the more you will learn, and the more confident you will become. Once you have several projects that you can point to as good examples of your skillset as a data scientist, you are ready to enter the field.

How can I learn data science on my own?

It is possible to learn data science on your own, as long as you stay focused and motivated. Luckily, there are a lot of online courses and boot camps available. Start by determining what interests you about data science. If you gravitate to visualizations, begin learning about them. Starting with something that excites you will motivate you to take that first step. If you are not sure where you want to start, try starting with learning Python. It is an excellent introduction to programming languages and will be useful as a data scientist. Begin by working through tutorials or Udemy courses on the topic of your choice. Once you have developed a base in the skills that interest you, it can help to talk with someone in the field. Find out what skills employers are looking for and continue to learn those skills. When learning on your own, setting practical learning goals can keep you motivated.

Does data science require coding?

The jury is still out on this one. Some people believe that it is possible to become a data scientist without knowing how to code, but others disagree. A lot of algorithms have been developed and optimized in the field. You could argue that it is more important to understand how to use the algorithms than how to code them yourself. As the field grows, more platforms are available that automate much of the process. However, as it stands now, employers are primarily looking for people who can code, and you need basic programming skills. The data scientist role is continuing to evolve, so that might not be true in the future. The best advice would be to find the path that fits your skillset.

What skills should a data scientist know?

A data scientist requires many skills. They need a strong understanding of statistical analysis and mathematics, which are essential pillars of data science. A good understanding of these concepts will help you understand the basic premises of data science. Familiarity with machine learning is also important. Machine learning is a valuable tool to find patterns in large data sets. To manage large data sets, data scientists must be familiar with databases. Structured query language (SQL) is a must-have skill for data scientists. However, nonrelational databases (NoSQL) are growing in popularity, so a greater understanding of database structures is beneficial. The dominant programming language in Data Science is Python — although R is also popular. A basis in at least one of these languages is a good starting point. Finally, to communicate findings, data scientists require knowledge of visualizations. Data visualizations allow them to share complex data in an accessible manner.

Is data science a good career?

The demand for data scientists is growing. We do not just have data scientists; we have data engineers, data administrators, and analytics managers. The jobs also generally pay well. This might make you wonder if it would be a promising career for you. A better understanding of the type of work a data scientist does can help you understand if it might be the path for you. First and foremost, you must think analytically. Data science is about gaining a more in-depth understanding of info through data. Do you fact-check information and enjoy diving into the statistics? Although the actual work may be quite technical, the findings still need to be communicated. Can you explain complex findings to someone who does not have a technical background? Many data scientists work in cross-functional teams and must share their results with people with very different backgrounds. If this sounds like a great work environment, then it might be a promising career for you.

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If you are ready to learn the “Machine Learning and Deep Learning A-Z: Hands-On Python ” course.

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