we learn lots of dataset how to predict values using different machine learning algorithm. problem-solving oriented subject that learns to apply scientific techniques to practical problems. The course orients on practical classes and self-study during preparation of datasets and programming of data analysis tasks. This course takes you through all the important modules that you need to know about, including machine learning and programming languages.
Genetic Algorithms (GAs) are members of a general class of optimization algorithms, known as Evolutionary Algorithms (EAs), which simulate a fictional environment based on theory of evolution to deal with various types of mathematical problem, especially those related to optimization. Also Genetic Algorithms can be categorized as a subset of Metaheuristics, which are general-purpose tools and algorithms to solve optimization and unsupervised learning problems.
In this series of video tutorials, we are going to learn about Genetic Algorithms, from theory to implementation. After having a brief review of theories behind EA and GA, two main versions of genetic algorithms, namely Binary Genetic Algorithm and Real-coded Genetic Algorithm, are implemented from scratch and line-by-line, using both Python and MATLAB. This course is instructed by Dr. Mostapha Kalami Heris, who has years of practical work and active teaching in the field of computational intelligence.
Components of the genetic algorithms, such as initialization, parent selection, crossover, mutation, sorting and selection, are discussed in this tutorials, and backed by practical implementation. Theoretical concepts of these operators and components can be understood very well using this practical and hands-on approach.
At the end of this course, you will be fully familiar with concepts of evolutionary computation and will be able to implement genetic algorithms from scratch and also, utilize them to solve your own optimization problems.
problem solving – put everything together with software
High school mathematics and physics.
Programming is one aspect of computer science and software engineering. The primary goal of this course is to build a solid foundation of programming knowledge and skills. With what learned in this course, the students should find it is easier to learn more advanced concepts in computer science.
Not everyone will be or want to be a software engineer, however, this course can help them realize how a problem can be solved by using computer program; how Python can help scientists and engineers improve their productivity.
Believe or not, software developers usually join a product development from the very beginning to the very end. (while this is not true for mechanical engineers or electrical engineers). Most importantly, sometimes, updating software is the better solution to fix or improve a product.
The teaching can be viewed as a vehicle to help students develop problem solving skills. This course will use some mathematics or physics, but it is not a math or physics course, and we use them in programming to re-enforce the learning in those fields.
At the end of this course, It would be a great achievement for the students and me when they find they are able to learn some other programming languages or computer science topics not taught in this course by themselves
Who this course is for:
High school students who are thinking CS as their major.
In QuantConnect’s Boot Camp tutorial series you’ll learn the tools for quantitative trading. You’ll build skills in finance, statistics, and software development while learning about QuantConnect’s API with code-along tasks. After this course, you’ll be able to implement your own trading strategies in python and have a foundation in robust algorithm design.
We’ll start out with the fundamentals for individual algorithm creation and move on to building an institutional-grade system using the Algorithm Framework. You’ll be able to use its architecture to deploy your own flexible investment strategies.
In each lesson, we’ll code together on QuantConnect’s integrated development environment to create algorithms that you can backtest and use. You’ll manage your portfolio, use indicators in technical trading strategies, trade on universes of assets, automate trades based on market behavior, and understand how data moves in and out of your algorithm.
QuantConnect is one of the largest quantitative trading communities in the world. Part of what makes it so special is the diverse backgrounds in the community. We’re so excited to make these skills accessible to you so you can implement your own unique ideas. Hope to see you in the first lesson!
Who this course is for:
Python developers who want to build trading algorithms.
Learn Python programming, data structures, and algorithms faster. Understand the basics of object-orientated programming, data structures, and algorithms. The best examples to study Python are indeed data structures and algorithms. When you understand precisely what are data structures and algorithms, you will be able to implement advanced data structures and algorithms used while building software.
With this knowledge course, you can start using Python documentation very easily. You can jump-start in building advanced projects. The course is purposefully short to cover as much knowledge as possible. This saves the time required to learn.
There are many algorithms and data structures, but understanding the basics of data structures and algorithms from this course should enable you to be proficient with all of them faster, as the basics of algorithmic thinking are the same. The main idea of this course is to provide a basic foundation for programming in Python, especially object-oriented programming. This is thanks to explanations of the basics.
Abstraction stops being an abstraction – you can imagine each abstract programming concept that is described in the materials. With this course, the learning curve is much faster. The course is suitable for beginners who want to gain advanced knowledge but can be also helpful for advanced learners to complete their knowledge.
If you want to gain additional knowledge on Python, data structures, and algorithms go to other courses or books. There you can find implementations of advanced Python concepts, data structures and algorithms.
A beginner Python course covering all of the basics you need to know
Learn the basics of Python in a quick and easy-to-understand course! This course will teach you the fundamentals of Python and covers practice problems so you can advance your Python skills quickly. You will learn about installing / setting up Python, input, output, variables, data types, converting between data types, strings, numbers, arithmetic, conditional statements, loops, lists, tuples, and functions.
Installing and Setting up Python
Input / Output
Converting between Data Types
Working with Strings
Working with Numbers (Arithmetic, Math Functions, etc.)
Loops (While, For)
Pycharm (Platform for coding in Python)
Note: The first video of the course will teach you how to set up Python and Pycharm so you can begin coding your problems right away! The course assumes you have no prior knowledge of Python or programming.
Information about the Instructor:
AlgoSTEM is a non-profit organization led by Arushi Gupta and Akshaj Gupta that aims to increase accessibility to STEM education. Through its free online courses, AlgoSTEM has taught over 35,000 students worldwide. AlgoSTEM instructors are experienced and knowledgeable about the subjects they teach which include computer science, math, and various sciences.
Along with having multiple Udemy courses, AlgoSTEM has a popular YouTube channel called Algorythm that covers solutions to coding problems including those from Leetcode, Codeforces, Codechef, and various math competitions.
If you need a quick brush-up, or learning Python for the first time, you’ve come to the right place!
Let’s get started learning one of the most easiest coding languages out there right now. There’s no need to fret if you haven’t coded before. By the time you finish this course, you’ll be a pro at Python!
Python is a great and friendly language to use and learn. It fun, and can be adapted to both small and large projects. Python will cut your development time greatly and overall, its much faster to write Python than other languages. This course will be a quick way to understand all the major concepts of Python programming. You’ll be a whiz in no time.
This course is a one-stop-shop for everything you’ll need to know to get started with Python, along with a few incentives. We’ll begin with the basics of Python, learning about strings, variables, and getting to know the data types. We’ll soon move on to the loops and conditions in Python. Afterwards, we’ll discuss a bit of file manipulation and functions. By then, you’ll know all the basics of Python.
I hope you’re excited to dive into the World of Python with this course. Well, what are you waiting for? Let’s get started!
Who this course is for:
Even if you haven’t touched coding before, it won’t matter. The easy step-to-step lectures will quickly guide you through everything you’ll need to know about coding, mainly Python. This course is here for you to get accustomed and familiar with Python and its syntax. And above all, Python is one of the easiest coding languages to learn, and there’s a lot you can do with it.
The main purpose of this course is to give students the ability to analyze and present data by using Azure Machine Learning, and to provide an introduction to the use of machine learning and big data.
By- Uditha Bandara is specializes in Microsoft Development technologies. He is the South East Asia`s First XNA/DirectX MVP (Most Valuable Professional). He had delivered sessions at various events and conferences in Hong Kong, Malaysia, Singapore, Sri Lanka and India.
How does Machine Learning build a Linear Regression model?
Some knowledge of programming in any language is essential.
Machine Learning is becoming ubiquitous across all industries. Already many applications have been identified which use Machine Learning now. Few examples include Spam Detection, Face Recognition, Emotion Analysis, Object Detection, Credit Card Fraud Detection, Weather Prediction, and the list is almost endless. More new applications are being identified by different industries almost everyday.
It is not just about applying superior technology for traditional problems when we apply Machine Learning. It is also about business sense since applying Machine Learning, we can make experiments and applications much more economical.
This course is a result of a discussion among my Project Team from our cohort in IIT, Kanpur learning Cyber Security. We have embarked to create a product for Malware Detection using Machine Learning. While all of us are getting grips on Malware Analysis, the team needed some inputs of Machine Learning. To fill the gap, I conducted some sessions with our Project Team members on Machine Learning. This course is a collection of the recording of these sessions.
This course discusses what are Machine Learning Algorithms. We discuss Random Forest Algorithm and Linear Regression as examples to understand what are models in Machine Learning. We see how to implement such models using Python. During the discussion on the development of the Machine Learning models, we discuss the various steps like Data Preprocessing, Normalisation, Scaling, etc. We touch upon the basics of Neural Network and take a slight deep dive into Regression. The course includes discussion on concepts like what is overfitting, what is hyper-parameter tuning, etc.
This course tries to give an idea for what it takes to create a product which uses Machine Learning. I believe that the discussions can get one started to apply Machine Learning to many problems.
This course is especially for beginners who want to get started their journey in the field of machine learning. This course provides the hands-on experience with the python and scikit learn. So if you are new to the machine learning Get started with this course will be a good choice.
Who this course is for:
Begginer Python developers who want to get started with Machine Learning