Have you always wanted to learn programming but di... Moredn't know where to start ? Well now you are at the right place ! I created this python course to help everyone learn all the basics of this programming language. This course is really straight to the point and will give you all the notion about python. Also, the course is not that long so and the way the material is presented is very easy to assimilate. So if python is something that you are interested about, then you will definitely like this course.
Enjoy your learning :)
Who this course is for:
People interested to learn how to program in python
people curious about programming Less
Every other course online teaches you the basic sy... Morentax and features of Typescript, but only this course will show you how to apply Typescript on real projects, instructing you how to build large, successful projects through example.
ES6 is the 6th edition, officially known as ECMAScript 2015, and was finalised in June 2015.
ES6 adds significant new syntax for writing complex applications, including classes and modules, but defines them semantically in the same terms as ECMAScript 5 strict mode. Browser support for ES6 is still incomplete.
Do you need to master the art of front-end develop... Morement?
Look no further. This course is your complete beginners guide to developing cutting-edge web pages that are fully mobile responsive.
The course branches into three sections.
1. Explore HTML5
Learn the composition of a web page and how a web browser interprets html code to display the visual elements of a page. Learn the core fundamental aspects of HTML syntax, to ensure you are well prepared for the remaining sections ahead.
2. Explore CSS3
Learn to add stunning design elements to really make web pages visually aesthetic. Learn a broad range of CSS attributes to make web pages completely mobile responsive, even on the trickiest of devices such as phones and tablets.
Who this course is for:
Anyone who needs to learn to code
Anyone who needs to build a website Less
Learn to do an SEO audit with my comprehensive 50-... Morepoint checklist. You will be able to not only identify problems on your site, but I'll show you how to fix them on your own.
My goal is for you to be able to find and fix SEO problems on your site. You will only need to hire freelancers to do the intermediate to advanced technical work that is beyond SEO.
Sometimes you will encounter SEO issues that can be fixed by a software engineer or a network engineer. In those cases, I recommend that you hire a freelancer. But in most cases, I show you how to fix the SEO problems on your own.
After The Audit, You Will Have:
After performing the audit, you'll have a list of improvements and an ability to implement:
Making your site mobile
Improving site load speed
Optimal content strategy with full site crawling, indexing, and ranking potential
Sell SEO Audit Services As A Freelancer Or As An Agency Service
SEO audits are a popular service to provide. After this course, you'll be able to impress potential clients that you will give them an impressive and comprehensive site audit with many actionable items they can implement. After that, you will be able to implement most of those action items and make more revenue from that.
Who this course is for:
Website owners, entrepreneurs, freelancers Less
In this course, you will learn how to automate you... Morer legal documents using Documate. The McKinsey Global Institute found that 23% of a lawyer's job can be automated. Legal documents can be tedious and repetitive. However, with the right approach, you can automate the creation of legal documents.
Documate is one of the most powerful technologies to improve your legal practice. Attorneys that embrace document automation will enjoy tremendous benefits resulting from their new competitive advantage.
Get started automating your documents! Less
Have you already got some experience in the C prog... Moreramming language but want to take it further? Then this course is for you.
This course will teach you all about creating internal data structures in C.
This course will teach you how to create the following:
Linked List Implementation
Double Linked List Implementation
Array List Implementation
Binary Tree Implementation
All of the implementations described above will be created on video from scratch! You will learn how all of these work internally and when they should be used. This course is a "must have" for someone who has learned the fundamentals of the C Programming Language
Who this course is for:
C programmers who want to learn how to develop data structures in their applications Less
You're looking for a complete Convolutional Neural... More Network (CNN) course that teaches you everything you need to create a Image Recognition model in R, right?
You've found the right Convolutional Neural Networks course!
After completing this course you will be able to:
Identify the Image Recognition problems which can be solved using CNN Models.
Create CNN models in R using Keras and Tensorflow libraries and analyze their results.
Confidently practice, discuss and understand Deep Learning concepts
Have a clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16 etc.
How this course will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Convolutional Neural networks course.
If you are an Analyst or an ML scientist, or a student who wants to learn and apply Deep learning in Real world image recognition problems, this course will give you a solid base for that by teaching you some of the most advanced concepts of Deep Learning and their implementation in R without getting too Mathematical.
Why should you choose this course?
This course covers all the steps that one should take to create an image recognition model using Convolutional 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 300,000 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 (Section 2)- Setting up R and R Studio with R crash course
This part gets you started with R.
This section will help you set up the R and R studio on your system and it'll teach you how to perform some basic operations in R.
Part 2 (Section 3-6) - ANN 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 (Section 7-11) - Creating ANN model in R
In this part you will learn how to create ANN models in R.
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. 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 (Section 12) - CNN Theoretical Concepts
In this part you will learn about convolutional and pooling layers which are the building blocks of CNN models.
In this section, we will start with the basic theory of convolutional layer, stride, filters and feature maps. We also explain how gray-scale images are different from colored images. Lastly we discuss pooling layer which bring computational efficiency in our model.
Part 5 (Section 13-14) - Creating CNN model in R
In this part you will learn how to create CNN models in R.
We will take the same problem of recognizing fashion objects and apply CNN model to it. We will compare the performance of our CNN model with our ANN model and notice that the accuracy increases by 9-10% when we use CNN. However, this is not the end of it. We can further improve accuracy by using certain techniques which we explore in the next part.
Part 6 (Section 15-18) - End-to-End Image Recognition project in R
In this section we build a complete image recognition project on colored images.
We take a Kaggle image recognition competition and build CNN model to solve it. With a simple model we achieve nearly 70% accuracy on test set. Then we learn concepts like Data Augmentation and Transfer Learning which help us improve accuracy level from 70% to nearly 97% (as good as the winners of that competition).
By the end of this course, your confidence in creating a Convolutional Neural Network model in R will soar. You'll have a thorough understanding of how to use CNN to create predictive models and solve image recognition 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 R for Deep Learning?
Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Deep learning in R
1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing.
2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind.
3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science.
4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R.
5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science.
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.
Who this course is for:
People pursuing a career in data science
Working Professionals beginning their Deep Learning journey
Anyone curious to master image recognition from Beginner level in short span of time Less