Udemy Free Courses
Cryptocurrency Course: Learn to Make Money Online WORLDWIDE!
Learn a Step-By-Step Strategy for Making Money with Cryptocurrencies!
Detailed Description of the Course:
Last years have been HUGE for cryptocurrencies. Many millionaires were made all over the world. Technologies went forward and new innovations were created.
On the other hand, many people lost money because they didn’t know what they were doing. They threw money here and there because other people were doing so.
If you want to make money with cryptocurrencies you need to learn important action steps that I teach you in this course. In this course, I will teach you step-by-step strategies for making money with cryptocurrencies.
This cryptocurrency opportunity is huge. You can make lots of money if you know if you have the skills.
If you don’t educate yourself, you put yourself in a huge danger of losing your money like millions of people have done.
One powerful idea from this course can be worth $1,000’s, $10,000’s or even $100,000’s for you in the long run!
Python Programming Complete Beginner Course Bootcamp
Learn Python Programming. A Premium level course with over 500 examples! No prior knowledge is needed.
Detailed Description of the Course:
This course is for students that are not familiar with Python at all, NO PRIOR KNOWLEDGE is required.
And as well as for experienced programmers who are familiar with other languages, but are wishing to know Python as well.
If you are interested in becoming a Python Ninja, this course will provide you the right set of tools to program in Python as real professionals.
The course brings all core topics of Python in an perfectly ordered structured way, and as such, all topics will be broken down into these 4 parts:
- Theory lecture
- Example in code lecture
- Homework – Going over the assignments
- Homework – Resolving the assignments
Course’s Unique Approach:
1) The Course Is Based On Python Standard Coding Conventions:
‘Python Coding Conventions’ are a set of global rules of coding structure, that will be discussed deeply for each topic during the course, which will make the difference between ‘Medium’ and ‘Professional’ programmer. Naming Conventions is an important subtopic that is rarely discussed in online courses, but can easily be fail you in job interviews.
2) High Focus On Examples And Homework Across The Course:
This course is focused on what I was missing, back in the days when I took my Python Online course, EXAMPLES.
The course will bring you over 500 examples, and challenging homework assignments for each topic.
Compare your answers to instructor’s attached source coe at the end of every ‘practice’ session.
3) High Focus On Independent Programming – Observation On Every Possible Angle For Each Topic:
You will get lots of examples for each topic & sub-topic so don’t be surprised if you can handle complicated assignments in programming once you complete this course. During the homework, In some cases the students will be guided to look the answers in Google and Stack-overflow, an important skill that every professional programmer should have.
The Lecturer of the course, Dmitry, is an experienced Team Leader from the IT / Fintech industry.
Join and be a part of the future, learn all Pythons core topics and become a Python Ninja.
Course’s Syllabus is the following :
- Installations of PyCharm – Most popular IDE (Where we write our code)
- Installations of Python , step-by-step configurations
- Variables – Strings
- Variables – String Formatting
- Variables – Integers
- Variables – Float
- Comparison Operators & Boolean Variables
- Collections – List
- Collections – Dictionary
- Collections – Set
- Collections – Tuple
- Conditions – ‘If’ and ‘Else’ Statements
- Loops – While
- Loops – For
- Objects Oriented Programming – Method
- Objects Oriented Programming – Classes
- Objects Oriented Programming – Inheritance
- Exception Handling – Try / Exception
- Exception Handling – Types Of Exceptions
- Version Control – Github
~ ~ ~ If you love the course, I ask you kindly to leave a 5 STAR review ~ ~ ~
*** The recommended resolution to watch course’s videos is 1080 HD ***
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Detailed Description of the Course:
We live in a vibrant and ever changing world, and this demands that our code reflects that, our code should be easy to read, understand and change!
Yet, a lot of developers still write code that is bad, even though it works, it lacks the best practices that keep the code easy to maintain and modify.
Specifically, you will learn about:
- Common issues, known as code smells.
- Techniques to refactor already existing code.
- Tools that enforce you to follow the best practices and good rules.
In this course you can find a compilation of best practices, rules and concepts that will help you with just that.
All this is backed up by examples and code snippets.
This course does NOT focus on a specific programming style or paradigm (like functional programming or object oriented programming) but instead covers general concepts and techniques that will apply regardless of the paradigm.
This course also comes with:
A responsive instructor in the Q&A Section
Udemy Certificate of Completion Ready for Download
Udemy’s 30 Day “No Questions Asked” Money Back Guarantee
If you get stuck you can benefit from a fast and direct support. You can contact me anytime!
See you inside!
Artificial Neural Networks (ANN) with Keras in Python and R
Understand Deep Learning and build Neural Networks using TensorFlow 2.0 and Keras in Python and R
Detailed Description of the Course:
You’re looking for a complete Course on Deep Learning using Keras and Tensorflow that teaches you everything you need to create a Neural Network model in Python and R, right?
You’ve found the right Neural Networks course!
After completing this course you will be able to:
- Identify the business problem which can be solved using Neural network Models.
- Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc.
- Create Neural network models in Python and R using Keras and Tensorflow libraries and analyze their results.
- Confidently practice, discuss and understand Deep Learning concepts
How this course will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course.
If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in Python without getting too Mathematical.
Why should you choose this course?
This course covers all the steps that one should take to create a predictive model using Neural Networks.
Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course
We are also the creators of some of the most popular online courses – with over 250,000 enrollments and thousands of 5-star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman – Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price. – Daisy
Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.
Download Practice files, take Practice test, and complete Assignments
With each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning.
What is covered in this course?
This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.
Below are the course contents of this course on ANN:
- Part 1 – Python and R basics
This part gets you started with Python.
This part will help you set up the python and Jupyter environment on your system and it’ll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.
- Part 2 – Theoretical Concepts
This part will give you a solid understanding of concepts involved in Neural Networks.
In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.
- Part 3 – Creating Regression and Classification ANN model in Python and R
In this part you will learn how to create ANN models in Python.
We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models.
We also understand the importance of libraries such as Keras and TensorFlow in this part.
- Part 4 – Data Preprocessing
In this part you will learn what actions you need to take to prepare Data for the analysis, these steps are very important for creating a meaningful.
In this section, we will start with the basic theory of decision tree then we cover data pre-processing topics like missing value imputation, variable transformation and Test-Train split.
By the end of this course, your confidence in creating a Neural Network model in Python will soar. You’ll have a thorough understanding of how to use ANN to create predictive models and solve business problems.
Go ahead and click the enroll button, and I’ll see you in lesson 1!
Below are some popular FAQs of students who want to start their Deep learning journey-
Why use Python for Deep Learning?
Understanding Python is one of the valuable skills needed for a career in Deep Learning.
Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:
In 2016, it overtook R on Kaggle, the premier platform for data science competitions.
In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.
In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.
Deep Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.
What is the difference between Data Mining, Machine Learning, and Deep Learning?
Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.
Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.
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