The Ionic Framework is supported on a variety of m... Moreobile (and desktop) platforms. In this course, you will learn how to go from no code to your very own application ready for sale on the Apple App Store and Google Play Store.
Unlike many courses that spend a lot of time on background, this course will be fast-paced, with a minimum of fuss and fluff. It's all hands-on.
I expect you to have some basic understanding of web development. You should know what a div is, for example; and know how to create a button. You should have a decent grasp of JavaScript, but you don't need to be a master.
I don't expect you to be an expert in React, but you should grasp the basics of JSX, component architecture, state, and hooks. Other concepts will be explained as needed, as close to their use as I can get.
By the time you complete the course, you should have the confidence you need to create and deploy your own mobile app UI for iOS or Android.
It'll be a fast ride, so enroll now and hang on.
Who this course is for:
Any developer wanting to publish mobile applications with technology they already know and love. Less
The concept of Artificial Intelligence is used in ... Moresci-fiction movies to describe a virtual entity that crossed some critical threshold point and developed self-awareness. And like any good Hollywood movie, this entity will turn against humankind. OMG! It’s a great concept to fuel our basic survival fear.
As you may guess, things, in reality, are completely different. Artificial Intelligence is one of the biggest revolutions in the software industry. It is a mind-shift on how to develop software applications. Instead of using hard-coded rules for performing something, we let the machines learn things from data, decipher the complex patterns automatically and then use it for multiple use cases.
Who this course is for:
Anyone Interested to learn Machine Learning 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
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.
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!
Cheers
Start-Tech Academy
------------
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
You're looking for a complete Machine Learning cou... Morerse that can help you launch a flourishing career in the field of Data Science & Machine Learning, right?
You've found the right Machine Learning course!
After completing this course you will be able to:
· Confidently build predictive Machine Learning models to solve business problems and create business strategy
· Answer Machine Learning related interview questions
· Participate and perform in online Data Analytics competitions such as Kaggle competitions
Check out the table of contents below to see what all Machine Learning models you are going to learn.
How this course will help you?
A 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 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.
Why should you choose this course?
This course covers all the steps that one should take while solving a business problem through linear regression.
Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your 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 machine 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 150,000 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.
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. Each section contains a practice assignment for you to practically implement your learning.
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.
What are the steps I should follow to be able to build a Machine Learning model?
You can divide your learning process into 3 parts:
Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.
Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model
Programming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python
Understanding of models - Fifth and sixth section cover Classification models and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.
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.
Who this course is for:
People pursuing a career in data science
Working Professionals beginning their Data journey
Statisticians needing more practical experience Less
You're looking for a complete Support Vector Machi... Morenes course that teaches you everything you need to create a SVM model in R, right?
You've found the right Support Vector Machines techniques course!
How this course will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning advanced course.
If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the advanced technique of machine learning, which are Support Vector Machines.
Why should you choose this course?
This course covers all the steps that one should take while solving a business problem through SVM.
Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your 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 machine 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 150,000 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.
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. Each section contains a practice assignment for you to practically implement your learning.
Go ahead and click the enroll button, and I'll see you in lesson 1!
Cheers
Start-Tech Academy
Who this course is for:
People pursuing a career in data science
Working Professionals beginning their Data journey
Statisticians needing more practical experience
Anyone curious to master SVM technique from Beginner to Advanced in short span of time Less
The growth in the technological field is exponenti... Moreal. Day by day, the IT sector is flourishing to a greater extent. With such rapid growth, the need for security is also increasing. People have started becoming concerned about their privacy. Cybersecurity issues are becoming a day-to-day struggle for businesses. Recent trends and cyber security statistics reveal a huge increase in hacked and breached data from sources that are common in the workplace. With these growing trends, every individual must be aware of security and that is why you should go for this course!
Most of the people are often confused when they are asked - “WHAT IS CYBER SECURITY?” The most common reply is - “UM, HACKING. ” Well, hacking is part of cyber security but it really doesn’t mean that hacking is the synonym of cyber security. Many courses out there are only focusing on the practical approach of cyber security - HACKING. Well, hacking is not the only domain of cyber security. There are other domains also, for example Incident Response Team, Blue team, Security Audits, IT Security Management, Security laws and many more. In order to start practicing hacking, you should first know the concepts! You should understand how the world is dealing with cyber threats. HACKING is not the only “coolest” thing here. There are other security topics as well.
!!!! Hackers attack every 39 seconds, on average 2,244 times a day. !!!!
So are you sure that your data is well protected from these hackers? Have you even configured your network to maintain your privacy? Do you know how hackers can gain access to your devices without you even getting notified? Well, don’t worry! We are here to answer these questions. There might be many questions in your mind regarding this course. We will surely address all of them once you enroll for this course, but for the time being, we’ll focus on the following questions:
WHY SHOULD I LEARN CYBER SECURITY?
Let’s face it. We live in a digital world. Our work lives, personal lives, and finances have all begun gravitating toward the world of the internet, mobile computing, and electronic media. Unfortunately, this widespread phenomenon makes us more vulnerable than ever to malicious attacks, invasions of privacy, fraud, and other such frightening cases. That’s why cyber security is such a vital part of a secure and well-ordered digital world. Cybersecurity keeps us safe from hackers, cyber criminals, and other agents of fraud.
But let me ask you a question,
HOW GOOD IS YOUR KNOWLEDGE ABOUT CYBER SECURITY?
Here are some facts that would blow up your mind!
According to Varonis, Worldwide spending on cyber security is forecasted to reach $133.7 billion in 2022.
Data breaches exposed 4.1 billion records in the first half of 2019.
Symantec says, the top malicious email attachment types are .doc and .dot which make up 37%, the next highest is .exe at 19.5%
These are not just facts but they are warnings! Everyone who is accessing a device should be aware of their security and that is why we have designed this course. Okay, here comes the next question.
UM, OKAY! BUT WHY SHOULD I ENROLL FOR THIS COURSE?
The perfect answer to this question would be - Have a look at our curriculum. THE FUNDAMENTALS OF CYBER SECURITY, is the first part of our CYBER SECURITY SPECIALIZATION. In this course, we have covered right from the basics of computer networking to the advance concepts such as cryptography, security auditing, incident management. Yes, we have actually started from the definition of a Computer Network. Our course is broadly divided into seven sections. In the first section we have covered the basics of networking. We have discussed different protocols and their usage. We have talked about IP addresses and their classes and a lot of things which are important from the point of view of cyber security.
Bonus Material - We have included practicals also!
From the second section we are actually talking about cyber security. Right from the history of cyber security to different terminologies have been discussed in this section. We have designed the curriculum in such a way that even a novice can understand cyber security and we are so sure that this course would actually give a better idea of cyber security. You will learn about critical thinking and its importance to pursue a career in Cyber security. You will also learn about organizations and resources to further research cyber security issues in the Modern era. This course is intended for anyone who wants to gain a basic understanding of Cyber security or as the first course in a series of courses to acquire the skills to work in the Cybersecurity field as a Jr Cybersecurity Analyst.
We recommend you to watch our introductory video where we have discussed the entire syllabus :) Have a look at few highlights from our course.
Handpicked curriculum, specially designed for all levels of learners.
Continuous assessment through challenging quizzes
Get your questions answered within 48 hours
A variety of resources such as useful links, books, PDFs are also provided
Regular updates related to cyber security
Exploration of different aspects of Cybersecurity
Practicals are also included along with theory
You will understand how cryptography works in real life
Concepts such as firewalls, antivirus, cyber attacks are also discussed and Many more.
We encourage suggestions
WHO CAN ENROLL FOR THIS COURSE?
This course is not intended for a specific group of people. Anyone who wants to learn about cyber security can enroll for this course. If you already know about security, you can always revisit the ideas and clear your doubts if you have any. We have not only covered the technical topics but also included all the statistics as well. For example we have discussed the scope of cyber security along with the career insights and also the salaries of different job roles in this field. This course will help you to decide whether you want to become a Penetration tester or security auditor or Red Team Specialist.
In short, this is the perfect course if you want to kick start your career in cyber security!
BOTTOM LINE -
Once you move ahead in this course, you will get a clear idea about cyber sec. The way we have put all the things together helps the learner in creating an interest in this field. We would like to assure one thing to you - THIS COURSE WILL LITERALLY TELL YOU EVERYTHING ABOUT CYBER SECURITY.
Happy Learning! Less
If you would like to learn Python 3 programming in... More 2020, you are going to LOVE this course! Get started with the most beginner friendly programming language and start writing your very own programs today!
We will cover the following topics in this course:
Introduction to Python and setup
Python programming basics
Functions - coding exercises
Lists, tuples and dictionaries
Files in Python 3
Error handling
Object oriented programming
Date & time
Regular expressions
Interacting with HTTP
Networking in Python 3
Threading
E-mails, PDFs, images
This course was designed for absolute beginners who wish to master the Python programming language. All lectures are downloadable for offline viewing. English subtitles and a certificate of completion are are available as well.
Thank you for taking the time to read this and we hope to see you in the course!
Who this course is for:
Students interested in learning the Python programming language.
Students who wish to pursue a career in software development. Less
This course will help you learn the basics as well... More as advanced levels of concepts and applications in Adobe Illustrator CC 2020. It covers real world applications of:
1. Logo Design
2. Packaging Design & 3D Mock-Up
3. Typography and Color Theory
4. Social Media Posting
Also, the tools of Adobe Illustrator have been thoroughly explained that will help you in creating the designs with much ease.
Salient Features of this Course:
A comprehensive course consisting of 179 video lectures that are organized in a pedagogical sequence.
Detailed explanation of all tools and commands used in the course.
Step-by-step instructions to guide the users through the learning process.
Practice Test
You will get a Certificate of Completion after completing the course. Less
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 :) Less
Although this course is for beginners, the knowled... Morege it provides is really advanced.
It is full of tips and tricks as well as tested methods that will make you an expert in digital marketing and online advertising.
Its curiculim includes:
Blogging
Social Media
Google Ads
SEO
Google Analytics
E-commerce Optimization
Conversion Optimization
Each section of the course is a step by step guide that not only gives you usefull instructions about the topic it addresses but also tried methods that you can easily use to bring profits to your business and to be a successful digital marketing professional. Less
Explore more about using Flexbox with examples and... More sample code to get you coding.
Flexbox is a one-dimensional layout method for laying out items in rows or columns. Items flex to fill additional space and shrink to fit into smaller spaces
Please note that the scope of this course using CSS Flexbox. If you are looking for a more detailed CSS course this course is not for you. Simple course with limited scope designed to be topic specific.
Taught by an instructor with over 20 years of Web Development experience.
If you've been looking to get started with CSS Flexbox - THIS COURSE IS FOR YOU!!!!
Nothing to lose - Fast friendly support is always available to help if you need it.
Please note that the SCOPE OF THIS COURSE IS Creating and using CSS flexbox and will not cover complex commands and everything about CSS, HOW TO GET STARTED COURSE - if you are looking at more detailed node or JavaScript content this is not for you.
Who this course is for:
Web developers
Web designers
Anyone who is building a website
Webmasters and web coders creating web content Less
Learn how to create and manage Kubernetes using on... Moree of the leading managed services on the market.
Azure Kubernetes Service Zero to Hero admin guide is an intermediate course which describes:
Comparison of Managed vs self-managed Kubernetes solutions;
Pros and Cons of Managed Kubernetes services;
AKS Capacity Analysis;
Detailed AKS network architecture;
How to create an AKS cluster using Azure Portal and Azure CLI;
Integrate AKS with Active Directory;
Create Kubernetes users using self-signed certificates;
How AKS creates and manages Azure storage resources;
AKS monitoring and log debugging.
Who this course is for:
Fearless Kubernetes administrators. Less
MERN stands for MongoDB, Express.js, React.js and ... MoreNode.js - and combined, these four technologies allow you to build amazing web applications
In this course we will be building FULL FLEDGED INSTAGRAM website and it will be a lots of fun as we building this together.
This course covers -
Building an backend API with Node.js & Express
Testing API using Postman
JWT based authentication
Image upload
Context API for state management
React Hooks
React Router hooks
Protecting routes or endpoints
Sending Emails
Forgot & Reset Password
This course is NOT an introduction course to React & Node js. It is fully hands on course for building full stacks websites using MERN Less
R Programming Language is not an easy language to ... Morelearn, and requires extensive practice in addition to the theory. Simply understanding in theory, how R Programming language works and everything that you can do with R is just not enough – you require a complete breakdown of how to go about doing it.
This is why we have designed this comprehensive project-based course! In this course, we attempt to break down this complex programming language and environment into an easy to follow structured tutorial that will help you not only understand this statistical language, but also become more familiar with how you can go about using it.
R is a programming language and environment for statistical computing and graphics. It allows developers to work with a range of statistical and graphical techniques including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, etc.
Our course will help you go through a step by process of understanding how R can help you become a more efficient data miner, analyst and statistician. However, it won’t just list or show you how to do that. The instructor will lead you through real world projects that will show you exactly how you can do them, while urging you to follow all the projects along with the instructor.
This project based course a great way for you to understand the fundamentals using a hands-on approach. No more confusing resources or boring theories, but rather you would actually get a hands on with the R Programming Language and environment.
In this course, you would learn the fundamentals of R programming language, including the basic concepts such as lists, functions, arrays, vectors, matrices, strings, etc.
There are five major aspects that you will learn in this course.
1. Practical approach to the R Programming – If you already have some background in R programming, or even have the knowledge, then this will help you gain a practical approach to R programming.
2. Learn Different Forms of Data Visualization – Visualizations of data has become a popular trend, as it makes the data more prominent and easier to understand. These include different types of visualizations such as bar graph, charts, heat map, etc.
3. Learn efficient ways to visualize data – Data should be efficient, especially if you are working with partial data. If the data is not efficient, the analysis would not be faulty and can be misunderstood.
4. Learn ways to manipulate data – Data isn’t always constant and it is often used to analyze past data and make future predictions. For this data is required to be manipulated to create predictions for multiple scenarios.
5. Learn to generate reports using R – Now we come to the most important stage of data mining and analysis. Here you will learn to generate effective reports that will help you put out a clean set of data analysis for consumption. Less
Would you like to get started as an ethical hacker... More? Do you want to become a professional penetration tester? Enroll now in The Complete Ethical Hacking Course and learn how to think like a hacker, and become familiar with the toolkit of a professional pentester. This course covers a wide range of topics relating to network security:
Introduction to ethical hacking
Reconnaissance
Scanning and enumeration
Network presence
Attacking systems
Web hacking
Social engineering
When you enroll in the course you will immediately receive access to 19+ hours of HD video tutorials, as well as additional supplemental resources for developing the necessary skills to succeed in the field. Learn by doing with demonstrations using popular pentesting tools such as Maltego, FOCA, Recon-ng, Nmap, masscan, tcpdump, Wireshark, Ettercap, Burp Suite, Scapy, Mimikatz, Hashcat, Konboot, Hydra, OWASP, SQLmap, mitmproxy, Skipfish and more!
Thank you for taking the time to read this, and we hope to see you in the course! Less
You're looking for a complete Decision tree course... More that teaches you everything you need to create a Decision tree/ Random Forest/ XGBoost model in Python, right?
You've found the right Decision Trees and tree based advanced techniques course!
After completing this course you will be able to:
Identify the business problem which can be solved using Decision tree/ Random Forest/ XGBoost of Machine Learning.
Have a clear understanding of Advanced Decision tree based algorithms such as Random Forest, Bagging, AdaBoost and XGBoost
Create a tree based (Decision tree, Random Forest, Bagging, AdaBoost and XGBoost) model in Python and analyze its result.
Confidently practice, discuss and understand Machine Learning concepts
How this course will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning advanced course.
If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the advanced technique of machine learning, which are Decision tree, Random Forest, Bagging, AdaBoost and XGBoost.
Why should you choose this course?
This course covers all the steps that one should take while solving a business problem through Decision tree.
Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your 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 machine 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 150,000 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.
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. Each section contains a practice assignment for you to practically implement your learning.
What is covered in this course?
This course teaches you all the steps of creating a decision tree based model, which are some of the most popular Machine Learning model, to solve business problems.
Below are the course contents of this course on Linear Regression:
Section 1 - 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 2 - 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.
Section 3 - Pre-processing and Simple Decision trees
In this section you will learn what actions you need to take to prepare it 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. In the end we will create and plot a simple Regression decision tree.
Section 4 - Simple Classification Tree
This section we will expand our knowledge of regression Decision tree to classification trees, we will also learn how to create a classification tree in Python
Section 5, 6 and 7 - 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. In this course we will discuss Random Forest, Baggind, Gradient Boosting, AdaBoost and XGBoost.
By the end of this course, your confidence in creating a Decision tree model in Python will soar. You'll have a thorough understanding of how to use Decision tree modelling to create predictive models and solve business problems.
Go ahead and click the enroll button, and I'll see you in lesson 1!
Cheers
Start-Tech Academy
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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.
What are the steps I should follow to be able to build a Machine Learning model?
You can divide your learning process into 4 parts:
Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.
Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model
Programming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python
Understanding of Linear Regression modelling - Having a good knowledge of Linear Regression gives you a solid understanding of how machine learning works. Even though Linear regression is the simplest technique of Machine learning, it is still the most popular one with fairly good prediction ability. Fifth and sixth section cover Linear regression topic end-to-end and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.
Why use Python for data 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.
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 Data journey
Statisticians needing more practical experience
Anyone curious to master Decision Tree technique from Beginner to Advanced in short span of time Less
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