Free Udemy Course – 22 June 2021

Today’s Free Udemy Courses are; Machine Learning & Deep Learning in Python & R, The Python Developer Essentials 2021 Immersive Bootcamp, The Python Programming For Everyone Immersive Training and Python Programming Beyond The Basics & Intermediate Training

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Free Udemy Course – 22 June 2021

Free Udemy Course

Machine Learning & Deep Learning in Python & R

How this course will help you?

Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.

If you are a business manager or an executive, or a student who wants to learn and apply machine learning and deep learning concepts in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning and deep learning. You will also get exposure to data science and data analysis tools like R and Python.

Why should you choose this course?

This course covers all the steps that one should take while solving a business problem through linear regression. It also focuses Machine Learning and Deep Learning techniques in R and Python.

Most courses only focus on teaching how to run the data analysis but we believe that what happens before and after running data analysis is even more important i.e. before running data analysis it is very important that you have the right data and do some pre-processing on it. And after running data analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business. Here comes the importance of machine learning and deep learning. Knowledge on data analysis tools like R, Python play an important role in these fields of Machine Learning and Deep Learning.

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 machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course. We have an in-depth knowledge on Machine Learning and Deep Learning techniques using data science and data analysis tools R, Python.

We are also the creators of some of the most popular online courses – with over 600,000 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

Our Promise

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. We aim at providing best quality training on data science, machine learning, deep learning using R and Python through this machine learning course.

Download Practice files, take Quizzes, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts on data science, machine learning, deep learning using R and Python. Each section contains a practice assignment for you to practically implement your learning on data science, machine learning, deep learning using R and Python.

Table of Contents

  • Section 1 – Python basic

This section gets you started with Python.

This section 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. Python basics will lay foundation for gaining further knowledge on data science, machine learning and deep learning.

  • Section 2 – R basic

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. Similar to Python basics, R basics will lay foundation for gaining further knowledge on data science, machine learning and deep learning.

  • Section 3 – Basics of Statistics

This section is divided into five different lectures starting from types of data then types of statistics then graphical representations to describe the data and then a lecture on measures of center like mean median and mode and lastly measures of dispersion like range and standard deviation. This part of the course is instrumental in gaining knowledge data science, machine learning and deep learning in the later part of the course.

  • Section 4 – Introduction to Machine Learning

In this section we will learn – What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.

  • Section 5 – Data Preprocessing

In this section you will learn what actions you need to take step by step to get the data and then prepare it for the analysis these steps are very important. We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bivariate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation.

  • Section 6 – Regression Model

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, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem.

  • Section 7 – Classification Models

This section starts with Logistic regression and then covers Linear Discriminant Analysis and K-Nearest Neighbors.

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 performance using confusion matrix, how categorical variables in the independent variables dataset are interpreted in the results, test-train split and how do we finally interpret the result to find out the answer to a business problem.

  • Section 8 – Decision trees

In this section, we will start with the basic theory of decision tree then we will create and plot a simple Regression decision tree. Then we will expand our knowledge of regression Decision tree to classification trees, we will also learn how to create a classification tree in Python and R

  • Section 9 – Ensemble technique

In this section, we will start our discussion about advanced ensemble techniques for Decision trees. Ensembles techniques are used to improve the stability and accuracy of machine learning algorithms. We will discuss Random Forest, Bagging, Gradient Boosting, AdaBoost and XGBoost.

  • Section 10 – Support Vector Machines

SVM’s are unique models and stand out in terms of their conceptIn this section, we will discussion about support vector classifiers and support vector machines.

  • Section 11 – 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.

  • Section 12 – Creating ANN model in Python and R

In this part you will learn how to create ANN models in Python and 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.

  • Section 13 – 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.

  • Section 14 – Creating CNN model in Python and R

In this part you will learn how to create CNN models in Python and 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.

  • Section 15 – End-to-End Image Recognition project in Python and 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).

  • Section 16 – Pre-processing Time Series Data

In this section, you will learn how to visualize time series, perform feature engineering, do re-sampling of data, and various other tools to analyze and prepare the data for models

  • Section 17 – Time Series Forecasting

In this section, you will learn common time series models such as Auto-regression (AR), Moving Average (MA), ARMA, ARIMA, SARIMA and SARIMAX.

By the end of this course, your confidence in creating a Machine Learning or Deep Learning model in Python and R will soar. You’ll have a thorough understanding of how to use ML/ DL models to create predictive models and solve real world business problems.

Below is a list of popular FAQs of students who want to start their Machine learning journey-

What is Machine Learning?

Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

Why use Python for Machine Learning?

Understanding Python is one of the valuable skills needed for a career in Machine 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.

Machine 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.

Why use R for Machine Learning?

Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine 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.

Click for Free Udemy Course

The Python Developer Essentials 2021 Immersive Bootcamp

This course covers all the following concepts theoretically and practically:

  • How to Program with Modern Python
  • The clean Python code
  • The core Python concepts needed to become A Dev Professional
  • Understand of how Python works behind the scenes
  • Variables, Representing Data Types, and using Math
  • Syntax and Recognize Code Blocks
  • Understanding and Analyzing Errors
  • Built-In Data Structures
  • Built-In Functions and Methods
  • Flows Control for Programs
  • User-Defined and Anonymous Functions
  • Object-Oriented Programming
  • Different types of Modules
  • Handle Files using Python 3.9.2
  • Get the Instructor QA support

All of that and many more supported by the instructor assistance and guidance throughout this complete high-quality Guide.

Whether you are a beginner or a professional, this Guide is for you, and will change your Dev career and your thinking for the best in the world of software Engineering.

Python is the most popular programming language out there, and The best programming language in terms of ease and features.

So, what are you waiting for, enroll now to go through an Immersive Training of the most popular Programming Language on the market, Python.

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The Python Programming For Everyone Immersive Training

Welcome to The Python Programming For Everyone Immersive Training Course for Beginners.

This Immersive Masterclass covers all the essential topics to become a Professional Python developer from the ground up

Topics like: variables, data types, Strings, data structures,  functional programming, different types of modules, files handling, object-oriented programming and many more.

You’ll get A demonstration of each point in this training and an explanation of all theoretical and practical aspects in an easy way and in an easy language for anyone.

Also, you can test your skills using quizzes and be a certified python developer that can be hired and you can upload the certificate of completion to your profile.
Python is one of the coolest, and best programming languages in terms of ease and features.

It is very easy for you to read the Python code, as if you were reading a regular English sentence.

The Python language can work with everything indisputably in many areas.

With Python, It is possible to do everything you imagine in the world of programming and data.

Python can work in areas such as:

Data Science.

Machine Learning.

Deep Learning.

Artificial intelligence.

Ethical Hacking.

Blockchain Applications.

Web Scraping.

Web Applications.

Mobile Applications.

Desktop Applications.

Games Applications.

Browser Extensions.

And many other fields.

And you’ll get a full support during this step by step course by the instructor if you encounter any problems or errors.

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Python Programming Beyond The Basics & Intermediate Training

Even if you are in any field such as data science, web development or machine learning, it is very necessary to know all the concepts that we will talk about in this course, as well as how to use them in a theoretical and practical way as we will do, and this is in order to facilitate the creation of programs in the correct way as you want it without wasting time or complication.

We created this course for you if you want to boost your Python career to become a productive Python programmer .

What are the topics that we will discuss in this course?

First, we will talk about iterators in the Python language, how to use them, the concepts and functions related to them, how to create them, and what is the purpose of creating them easily.

Simply, Iterator in Python is simply an object that can be iterated upon. An object which will return data, one element at a time.

This is a brief and simple definition of Python iterator.

Let’s go to the second section, which we will talk about.

In the second section we will talk about The Python scope Of all kinds and how to deal with them.

Not all variables or functions can be accessed from anywhere in a program. The part of a program where a variable or function is accessible is called its scope.

The section that next, we’ll talk about the string formatting To make sure a string will display as expected . and You’ll learn about these formatting techniques in detail and add them to your Python string formatting toolkit.

And in the fourth section we will know all about:
What generators in Python are and how to use them

How to build generator functions and expressions

How the Python yield statement works and the difference between yield and return.

How to use multiple Python yield statements in a generator function.

How to use advanced generator methods in your apps.

The section that next, you’ll learn everything about regular expressions in Python.

This is a very important topic, and we will talk about it in a detailed and practical way and with deep clarification. Actually, You’ll have all power of regular expressions, You will work with the re library, deal with pattern matching, and many more.
Basically, Regular Expressions are a tool for matching patterns in text. This is a brief and simple definition of Regular Expressions.

In the next section, you’ll master the most commonly used data structures from the Python collections module.
Basically, Collections in Python are containers that are used to store collections of data, for example, lists and dictionaries. These are built-in collections. Several modules have been developed that provide additional data structures to store collections of data. One such module is the Python collections module.

The collections module is used to improve the functionalities of the built-in collection containers.

The next section walks you through how to package a simple Python project. It will show you how to add the necessary files and structure to create the package, how to build the package, and how to use this Package.

In the next section, you’ll master all about math and statistics modules practically, and the functions that are used with them.

Simply, the math module provides access to the mathematical functions defined by the C standard. and The statistics module provides functions for calculating mathematical statistics of numeric data.

Finally, In the last part of this course you will learn something very important, which is decoration.

In this section on decorators, we’ll look at what they are and how to create and use them in detail.

Simply and By definition, a decorator is a function that takes another function and extends the behavior of the latter function without explicitly modifying it. that’s it . We will simplify this topic very without complication, and with practical examples to illustrate.

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