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[100% OFF FREE] Python And Flask Framework Complete Course For Beginners

Learn Python From Scratch I’ve created thorough, extensive, but easy to follow content which you’ll easily understand and absorb. The course starts with the basics, including Python fundamentals, programming, and user interaction. The curriculum is going to be very hands-on as we walk you from start to finish becoming a professional Python developer. We will start from the very beginning by teaching you Python basics and programming fundamentals, and then going into advanced topics and different career fields in Python so you can get real-life practice and be ready for the real world. The topics covered in this course are: Array implementation File methods Keywords and Identifiers Python Tuples Python Basics Python Fundamentals Data Structures Object-Oriented Programming with Python Functional Programming with Python Lambdas Decorators Generators Testing in Python Debugging Error Handling Regular Expressions Comprehensions Modules See you inside the course!

[100% OFF] Firebase ML Kit for Android Developer's Free

Firebase ML Kit for Android Developer's Make yo... Moreur Android Applications smart, use trained model or train your own models explore the power of AI and Machine Learning. This course was recorded using Android Studio 3.6.1 (which is a great introduction to the development environment!) For a smooth experience I'd recommend you use the same, but students can still use the latest Android Studio version available if they prefer! Wish you’d thought of Object Recognition/Face Detection/Text Recognition? Me too. But until I work out how to build a time machine. Here’s the next best thing. Firebase ML Kit for Android Developer's Why choose me? My name’s Hamza Asif, Udemy’s coding instructor. It's not my first on mobile Machine Leaning. I have a course named "Machine Learning for Android Developer using Tensorflow lite" on udemy. So which course you should take? It's recommended taking "Machine Learning for Android Developer using Tensorflow lite" first so that you can understand the working of Machine Learning. If you want to learn a practical implementation and use of Machine Learning in Android then that course is for you. This is my 2nd course on Android Machine Learning and I am the only udemy instructor with more than one course on that topic. My goal is to promote the use of Machine Learning in Android and I am excited to share my knowledge with you. Android Version we will use? Android Pie, Android Q All the Android Application we will develop in this course we will use Android Pie and Q to test them. So we are\ So join my Firebase ML Kit for Android Developer's course today and here’s what you’ll get Learn practical implementation of Text Recognition, Language Identification, Face and expression detection, Barcode scanning, Landmark Recognition, Text Translation, and Object detection and recognition. With Auto ML learn how to train the model on your own dataset and use those models in Android Application Learn about both on-device and Cloud Machine Learning Why take this course? Machine Learning use is at its peak so is the mobile tech but people having skills to implement both are rare. This course will enable you to empower your Android Application with the practical implementation of Machine Learning, Computer Vision and AI. Having a little knowledge of Android Development, this course will differentiate you from other developers because you will have something that is currently in demand. This course will make provide you a smooth path to become a pro in using Machine Learning in your Applications. This course will not just enable you to apply machine learning in limited scenarios but It will enable you to Prepare or download your own dataset Train machine learning model Develop Android Application So if you have very basic knowledge of Android Development and want to apply Machine Learning in Android Applications without knowing background knowledge of Machine Learning this course is or you. Is this course for you? This is a one-size-fits-all course for beginners to experts. So, this course is for you if you are: A total beginner, with a curious mind and a drive to make and create awesome stuff using App development and ML A fledgling developer, want to add Machine Learning implementation in his skillset A pro app developer-heavyweight, with an itch to build your dream app An entrepreneur with big ideas Benefits to you Risk-free! 30-day money-back guarantee Freedom to work from anywhere (beach, coffee shop, airport – anywhere with Wi-Fi) Potential to work with forward-thinking companies (from cool start-ups to pioneering tech firms) Rocket-fuelled job opportunities and powered-up career prospects A sense of accomplishment as you build amazing things Make any Android app you like (your imagination is your only limit) Submit your apps to Google Play and potentially start selling within hours Thanks for getting this far. I appreciate your time! I also hope you’re as excited to get started as I am to share the latest use of ML in Android development with you. All that remains to be said, is this… Don’t wait another moment. The world is moving fast. And I know you’ve got ideas worth sharing. Coding really can help you achieve your dreams. So click the button to sign up today – completely risk-free. And join me on this trailblazing adventure, today. Who this course is for: Anyone who wants to learn the practical implementation of Machine Learning and Computer Vision in their Android Applications. Anyone who wants to make their Application smart. Anyone who wants to train and deploy Machine Learning models on his own data without background knowledge of Machine Learning. Less
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[100% OFF] Fundamentals of Machine Learning[Hindi] Free

This course is designed to understand basic Concep... Moret of Machine Learning. Anyone can opt for this course. No prior understanding of Machine Learning is required. Data pre processing and Simple Linear Regression Concepts are covered in detail. Coding part is not covered, however wherever possible I have attached the code in the resources. Now question is why this course? This Course will not only teach you the basics of Machine learning and Simple Linear Regression. It will also cover in depth mathematical explanation of Cost function and use of Gradient Descent for Simple Linear Regression. Understanding these is must for a solid foundation before entering into Machine Learning World. This foundation will help you to understand all other algorithms and mathematics behind it. Less
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[100% OFF] Projects in R: Learn R Creating Data Science Projects Free

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
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[100% OFF] Socket Programming in Python | Socket Programming Course Free

Welcome to this course on Python Socket Programmin... Moreg Tutorial. In this video we will see the Basics of Networking. The goal of this Socket programming tutorial is to learn how to build client/server applications that communicate using sockets. We are going to look at a bunch of application level protocols (HTTP, DNS, FTP, SMTP, POP, IMAP, ..). The Objective of this course is to understand principles of networking, learn TCP and UDP features, learn to implement Python programs to access networking functionality. We will see Python examples of client and server program structures etc. In this Course you will learn Basics of Networking, Basics of Sockets | What is a Socket?, Write Your First Socket Program using Python socket module, TCP/IP Client and Server, Build a Python Socket Client with Example, UDP Client/Server Socket in Python with Example, Make a Multithreaded Socket Server, Socket Server with Multiple Clients Less
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[100% OFF] Learn Python Web Development With Flask Zero To Hero Free

Flask is a micro web framework written in Python. ... MoreIt is micro framework because it does not require dozens of tools and library. you can customize flask as you want with the help of flask and get creative with it. What will you learn? You will Learn about Back End Web Developed Flask Models Sqlalchemy Sqllite Python Advanced Databases Flask Login Login System Full CRUD functioning db Why Flask? Flask Make it easy to use and good for python beginners. it is best if you want to make project light weight Why this Course we will be making 2 different projects and 1 to work as assignment to do list app full blogging web app with login Project: Library Management System Who this course is for: Web Developers Beginners curious for python Beginners curious for python web development Beginners curious for python flask Beginners curious for python web frameworks Less
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[100% OFF] Deep Learning for Beginners: Neural Networks in R Studio Free

You're looking for a complete Artificial Neural Ne... Moretwork (ANN) course that teaches you everything you need to create a Neural Network model in R, right? You've found the right Neural Networks course! After completing this course you will be able to: Identify the business problem which can be solved using Neural network Models. Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc. Create Neural network models in R using Keras and Tensorflow libraries and analyze their results. Confidently practice, discuss and understand Deep Learning concepts How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course. If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in R Studio without getting too Mathematical. Why should you choose this course? This course covers all the steps that one should take to create a predictive model using 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 250,000 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 - Setting up R studio and 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 - 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 R In this part you will learn how to create ANN models in R Studio. 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 R will soar. You'll have a thorough understanding of how to use ANN 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 ------------ 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 Neural Network journey Statisticians needing more practical experience Anyone curious to master ANN from Beginner level in short span of time Less
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INVOICEZILLA PRO (100% DISCOUNT) (WINDOWS ORIGINAL)

YOUR DOWNLOAD Your download will start automatica... Morelly, or you can manually download from the link below. Download: SharewareOnSale_Giveaway_InvoiceZilla_PRO_hub.exe DIRECTIONS The download link for InvoiceZilla PRO is provided to you above. Your license key for InvoiceZilla PRO is also given above. Download and install InvoiceZilla PRO. After install, run InvoiceZilla PRO and register it with your license key. You can register it by going to ACTIVATION from within the main program window. Enjoy! Be sure to leave a nice comment if you like this offer or ask for help if you have any trouble. Less
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[100% OFF ] Learn Java by building 17 projects step by step Free

Learning to code can be both fun and rewarding . J... Moreava is a useful ,robust and popular programming language to embark on that journey. This course is a practical Hands-on course from the first to the last section. The best way to learn is by doing so you will be doing a lot of coding. Java is a case-sensitive, modern, general-purpose, object-oriented programming language. One of the reasons Java is so popular is that it is platform independent . You only need to write java code once and then run it on any platform be it mac, windows or Linux. In this course we will create 17 practical and useful projects step by step. The projects include: Calculator Digital Clock Weight Converter Temperature Converter Height Converter Random Number Generator BMI Calculator Tool Percentage Calculator Word Count Tool Add number program Program to determine Even numbers Program to determine Leap Year Program to calculate area and perimeter of a circle Program to find highest number Program to find Prime Numbers Program to create a triangle Program to determine largest numbers This course will guide you through java basics, its core syntax, and the fundamentals of writing strong java code. I will show you how to install Java JDK and NetBeans IDE. Netbeans is a great IDE for developing in java, and then dive into the language itself. Along the way, we will cover topics such as: Declaring and initializing variables with a variety of data types Exploring Basic Arithmetic operators Troubleshoot Compile errors Controlling flow with conditional code Creating Method Creating Event Handler methods Using Component controls and changing their properties If java is your first step into programming or adding it your existing skillset, these training videos will help you understand the language and use it to build your own great software. Less
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[100% OFF] Image Recognition using CNN: Keras & TensorFlow in R Studio Free

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
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[100% OFF] SVM for Beginners: Support Vector Machines in R Studio Free

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
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[100% OFF] Complete Machine Learning with R Studio - ML for 2020 Free

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. 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