Free Udemy Course – 1 May 2021

Today’s Free Udemy Courses are; Search Engine Optimization Complete Specialization Course, Neural Networks in Python: Deep Learning for Beginners, Image Recognition for Beginners using CNN in R Studio and Plan and Execute the Perfect Sprints in Agile and Scrum

Coupons are limited.  So It may run out early

Free Udemy Course – 1 May 2021

Free Udemy Course
Free Udemy Course

Search Engine Optimization Complete Specialization Course

The course cover everything from theory to practical with case studies and examples. This is the only course in the world where you woll also learn about the technicalities of SEO and how to handle them.

The content of this course is based on real world practices and checklists used by professionals in the SEO world.

You will learn about

  • Google Search engine
  • Google algorithms
  • WordPress development
  • Bootstrap
  • Shopify
  • Tools for SEO
  • On-page optimization
  • Technical SEO
  • Off-page Optimization
  • How to get a job in SEO?
  • How to start your own digital marketing company?

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Neural Networks in Python: Deep Learning for Beginners

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 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

    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.

  • Part 5 – Classic ML technique – Linear Regression
    This section starts with simple linear regression and then covers multiple linear regression.

    We have covered the basic theory behind each concept without getting too mathematical about it so that you

    understand where the concept is coming from and how it is important. But even if you don’t understand

    it,  it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.

    We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results and how do we finally interpret the result to find out the answer to a business problem.

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.

Click for Free Udemy Course

Image Recognition for Beginners using CNN in R Studio

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.

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Plan and Execute the Perfect Sprints in Agile and Scrum

Welcome to Plan and Execute the perfect Sprints in Agile and Scrum Course. Upon completion you will be able to:

  1. Create effective Product and Sprint plans,
  2. Map user requirements to developer tasks,
  3. Apply velocity-driven planning techniques,
  4. Generate work estimates for software products and much more, all from real-life and my personal experience,

By the end of the course, you will have gained an effective set of techniques to help you plan your next product development and to create a great software product, one that is managed right.

In this course, you will see how I deal with planning in Scrum. There are many other methods and they all perform well with Scrum as well as with other frameworks, methodologies, or in real life.

You will also have 3 individual assignments to do with detailed instructions on how to do it and with guidelines on how to do self-assessment. I’m asking you to do these assignments and believe me, you will collect huge rewards for doing this for yourself.

In this course, we will break down some specific problems and issues for Scrum at Work. I will also provide you with Tips and Best Practices and advice for how to handle the planning in Agile and Scrum, whether you are a Scrum Master or Product Owner, or developer.

Click for Free Udemy Course

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