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Our Machine Learning expert is good at followings: Knowledge of Understanding ML models, along with metrics to track their progress to that help to achieve business objectives. Writing and Analyzing the best Machine learning algorithms fit for data that could be used to solve a given problem and ranking them by their success probability Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world Verifying data quality, and/or ensuring it via data cleaning Supervising the data acquisition process if more data is needed Have knowledge of tons of available datasets online that could be used for training Defining validation strategies Defining the preprocessing or feature engineering on a given dataset Defining data augmentation pipelines Training models and tuning their hyper-parameters Analyzing the errors of the model and designing strategies to overcome them Deploying models to production As Good Machine Learning expert the Skills you expect. Proficiency with a deep learning framework such as NLP, TensorFlow or Keras Proficiency with Python and basic libraries for machine learning such as scikit-learn and pandas Expertise in visualizing and manipulating big datasets Proficiency with OpenCV Familiarity with Linux Ability to select hardware to run an ML model with the required latency. What is machine Learning and How it works About this Machine Learning As a beginner in machine learning, You always struggle to first understand the basic concepts and algorithms includes an introduction to Regression, Classification Analysis and Support Vector Machines in Machine Learning apart from having general programming experience in C and Java and some knowledge on algorithms. Machine learning is just one aspect of data science. You also need to know how to properly load data, clean the data, extract features, and finally - perform machine learning model training and testing. Machine Learning is used in most critical applications, such as data mining, natural language processing, image recognition, and expert systems. That solves those problems that cannot be solved by numerical means alone. Through Machine Learning it is possible quickly and automatically produce models that can analyze bigger, more complex data and deliver faster with more accurate results even on a very large scale. Evolution of machine learning Machine Learning is not like the basic training and testing model it's improving day-by-day with new computation powers and advanced pattern recognition. At the beginning, machine learning was born from pattern recognition and learning algorithms from which computers can learn without being programmed to perform specific tasks with the help of data; Data Scientists or researchers wanted to see if computers could learn from data and predict for new data based trained data model. The repetitive iterative aspect on data to train model again and again is important because of new data. Here are a few most common widely examples of machine learning applications you may be familiar with: The heavily hyped, self-driving Google car? The essence of machine learning. Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life. Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation. Fraud detection? One of the more obvious, important uses in our world today. What's required to create good machine learning systems? Data preparation capabilities. Algorithms – basic and advanced. Automation and iterative processes. Scalability. Ensemble modeling How does it work? Machine Learning is very similar to human way of learning any skills for example you want to learn python programming for instant. For claiming yourself as python programmer you need to do these three things. Python Book, online course or Python training from Python expert( That means Data) You have to focus on learning and have to learn the concept thoroughly the better you under the better you'll be programmer that means you are training yourself how to solve problems.(Training the model i.e you mind in this case) and this is final step after you have completed the all python topics, you need to pass test or examination so that you can claim yourself that you are able to solve python programming problems comfortable. but suppose there you got 90 marks out of 100. that mean your grasp or understanding on python is 90% accurate. any be other student get 70, 80, 96,97, 90 etc. these different student are separate algorithms. Any student get higher marks have more feasible solution. And the highest marked student solving methods will be used for prediction in future and to verify learning python skills well. They are made up of three major parts, which are: Model, Parameters and Learner. The Basic definition is: A Model is the system that makes a prediction or assumption on solution. The Parameters are the signals or factors used by the model to make the right decisions. A Learner is a system that adjusts the parameters and in turns the model by looking at differences in predictions versus actual outcomes. Should I learn Python or R for machine Learning? Just toss a coin, heads: Python tails: R There is probably an even split between those who recommend  Python & those who recommend R - so you might as well toss a coin. Both Python or R, are easy to learn and focus on work rather than syntax. but why people love python is that you already had been learning python programming to do basic stuff, Django, Python GUI and other so you'll feel comfortable to need extra effort to learn basic syntax or programming construct. But in the case of R, you suddenly come in the world of new syntax and very fews people or student would have used R for basic programming task before. So i'll suggest in first go use python and in future if need R you can switch easily Just dive right in any start learning Machine Learning is the basis for the most exciting careers in data analysis today. Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed. You’ll learn the models and methods and apply them to real world situations ranging from identifying trending news topics, to building recommendation engines, ranking sports teams and plotting the path of movie zombies. Machine learning tasks are typically classified into two broad categories, depending on whether there is a learning "signal" or "feedback" available to a learning system: Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs. As special cases, the input signal can be only partially available, or restricted to special feedback: Semi-supervised learning: the computer is given only an incomplete training signal: a training set with some (often many) of the target outputs missing. Active learning: the computer can only obtain training labels for a limited set of instances (based on a budget), and also has to optimize its choice of objects to acquire labels for. When used interactively, these can be presented to the user for labeling. Reinforcement learning: training data (in form of rewards and punishments) is given only as feedback to the program's actions in a dynamic environment, such as driving a vehicle or playing a game against an opponent. Classification: Spam filtering of emails. Regression: These algorithms also learn from the previous data like classification algorithms but it gives us the value as an output Example: Weather forecast – as how much rain will be there? Clustering: These algorithms use data and give output in the form of clusters of data. Example: Deciding the prices of house/land in a particular area (geographical location). Association: When you buy products from shopping sites, the system recommends another set of products. Association algorithms are used for this recommendation Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning). Methods include in assignment help: Support vector machines linear and logistic regression Tree classifiers Boosting Maximum likelihood and MAP inference EM algorithm Hidden Markov models Kalman filters k-means Gaussian mixture models Among others. Topics include for assignment help: Classification and regression Clustering methods, sequential models Matrix factorization Topic modeling and model selection. Did you know? In machine learning, a target is called a label. In statistics, a target is called a dependent variable. A variable in statistics is called a feature in machine learning. A transformation in statistics is called feature creation in machine learning. Most Popular Machine Learning Software Tools There are several Machine Learning Software that is available in the market. Enlisted below are the most popular ones among them. Scikit Learn Platform: Linux, Mac OS, Windows Cost: Free Written in language: Python, Cython, C, C++Classification Algorithms or Features: Regression, Clustering, Preprocessing, Model Selection, Dimensionality reduction. PyTorch Platform: Linux, Mac OS,, Windows Cost: Free Written in language: Python, C++, CUDA Algorithms or Features: Autograd Module, Optim Module, nn Module TensorFlow Platform: Linux, Mac OS, Windows Cost: Free Written in language: Python, C++, CUDA Algorithms or Features: Provides a library for dataflow programming. Weka Platform: Linux, Mac OS, Windows Cost: Free Written in language: Java Algorithms or Features: Data preparation, Classification, Regression, Clustering, Visualization, Association rules mining KNIME Platform: Linux, Mac OS, Windows Cost: Free Written in language: Java Algorithms or Features: Can work with large data volume. Supports text mining & image mining through plugins Colab Platform: Cloud Service Cost: Free Algorithms or Features: Supports libraries of PyTorch, Keras, TensorFlow, and OpenCV Apache Mahout Platform: Cross-platform Cost: Free Written in language: Java, Scala Algorithms or Features: Preprocessors, Regression, Clustering ,Recommenders, Distributed Linear Algebra. Accors.Net Platform: Cross-platform Cost: Free Written in language: C# Algorithms or Features: Classification, Regression, Distribution, Clustering, Hypothesis Tests &, Kernel Methods, Image, Audio & Signal. & Vision Shogun Platform: Windows, Linux, UNIX, Mac OS Cost: Free Written in language: C++ Algorithms or Features: Regression, Classification, Clustering,Support vector machines. Dimensionality reduction, Online learning etc. Keras.io Platform: Cross-platform Cost: Free Written in language: Python Algorithms or Features: API for neural networks, Rapid Miner, Cross-platform,Free plan List of Other Assignment Help Services Computer science Assignment help Python Programming Help Data Structures Assignment Help JAVA Assignment Help VB NET Development C++ Assignment Help C Programming Assignment Help SQL Homework Help Database Assignment Help HTML Assignment Help JavaScript Assignment Help Node js Assignment Help C# Assignment Help Android App Assignment Help MongoDB Assignment Help So, if you are still reading this and have an app idea, drop us a message, we can surely talk and discuss your project and get things done!. 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Okay Let’s jump to essence of this blog You might even be new to Programming or stuck in somewhere between in a programming problems . Either way, you have come to the right place, and chosen the right language! Why Python? Python is not only language ,it has its own kingdom. No language can be so easy as python. The syntax (words and structure) is extremely simple to read and follow, most of which can be understood even if you do not know any programming. What do we offer in Python Assignment Help, Python Homework Help Python GUI Assignment Help Codersarts ready to help any types of python related GUI like Tkinter, PyQT, PythonWx, and Kevy. Tkinter It used to provide better GUI interface to the user. Two main method used to create python GUI. TK : TK method used to create a main window Syntax: mainwindow =tkinter.TK() Tkinter Modules Before start the tkinter first need to import the tkinter module, you can used given below library to import the tkinter modules : import tkinter or from tkinter import * Example: In this example we describe basic structure of Tkinter to create GUI import tkinter m = tkinter.Tk() ''' widgets ''' m.mainloop() Geometry manager classes which used in Tkinter Basically three types of Geometry manager classes used in Tkinter. pack() grid() place() PyQT: This is the another way to create python GUI. PyQt is a cross-platform development framework. Qt is a popular C++ framework for writing GUI applications for all major desktop, mobile, Supporting Environment : PyQT is used to support different types of platform like - Linux Windows MacOS Android iOS etc Import Library : Given below library is used in PyQT to import module from PyQt5 import QtGui Basic Widgets which used in PyQT5 Here list of widgets which in PyQT5 QLabe QLineEdit QPushButton QRadioButton QCheckBox QComboBox QSpinBox etc. In this we connect widget with method using "connect". It is basically used to create better GUI than the other toolkits. WxPython: wxPython is a cross platform toolkit for creating desktop GUI applications in python. With wxPython developers can create applications on Windows, Mac and on various Unix systems. wxPython is a wrapper around wxWidgets. 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