Machine learning is having huge demand in the world. Beginners or students who are waiting for the best Applied machine learning online courses, then you people can choose this course. From this applied machine learning online course students will learn the topics like machine learning fundamentals, Python tools in the machine learning, supervised learning methods, K nearest neighbour, linear regression, logistic regression, decision tree, model selection and evaluation, metrics of basic evaluation, classifier decision functions, naive bayes classifier, neural networks, dimensionality reduction, gradient boosted decision trees, clustering, differences between the unsupervised and supervised methods, how to deal with missing data problems, OLS regression introduction, fundamentals of decision tree algorithms, naive bayes estimation in R and more. Students will enhance their skills in machine learning by building the real world projects. Our panel experts has picked some best applied machine learning online courses which are listed below.
Table of Contents
#1 Applied Machine Learning in Python by University of MICHIGAN – Coursera
The machine learning in the Python online course is offered by University of Michigan. Here already 107k+ candidates are learning this applied machine learning in python online course. Students will understand the differences between the descriptive statistics and machine learning. You will earn about the dimensionality of data, clustering data tasks, evaluation of clusters, creation of predictive models by using the supervised approaches etc. Learn about the prediction modeling methods like overfitting, cross validation etc. instructors will explain about the latest techniques like limitations of the predictive models and building the ensembles. You people can able to write the code by using Python language for analysis. Kevyn Collins Thompson is the instructor of this machine learning in python online course.
Key points:
- In the first module students will learn the topics like scikit introduction, basic machine learning concepts, workflow and tasks, know how to classify the problems based on the k nearest neighbors methods and know how to implement by utilizing the scikit learn library.
- You will also understand all the tools of python which are used in machine learning and learn about the classification of k nearest neighbors.
- From the second module you will learn the topics like supervised machine learning introduction, undefitting, overfitting, datasets of supervised learning, regression and classification of k nearest neighbour, linear regression, kernelized support vector machines etc.
- Instructor will explain the concepts of module 3 like model evaluation, model selection, classifier decision functions etc. from the module 4 you will learn the naive bayes classifier, neural networks, gradient boosted decision tree, clustering, data leakage etc.
Rating: 4.7 Out of 5
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#2 Applied Machine Learning in R – Udemy
The Machine Learning in the R online course is created by the Bogdan Anastasiei. Here more than 1k students are learning this machine learning in R online program. Instructor will explain all the essential topics of machine learning. You will execute the techniques of advanced regression analysis such as penalized regression, subset selection regression, and PLS regression,. Students can able to apply the techniques of complex classification like k nearest neighbour, decision trees, support vector machine and naive bayes. You people can conduct the cluster analysis for grouping the observations into the classes of homogeneous and can use the neural networks for the predictions. Instructor explains that how to carry out the logistic regression and also explain how to perform the discriminant analysis.
Key points:
- Students will understand about the machine learning, differences between the unsupervised and supervised methods, how to compute the prediction accuracy of the regression models, flexible models, restrictive models etc.
- From the cross validation section students will learn the topics like validation set approach, leave one out cross validation approaches and k fold cross validation approach.
- You people will learn the topics such as OLS regression introduction, validating the ols regression models, lasso regression,forward and backward selection regression, ridge regression, partial least square regressions and so on.
- From the logistic regression section you will understand the computing the prediction accuracy, ROC curve etc.
- Students will understand the concepts such as discriminant analysis, naive bayes estimation in r, k nearest neighbor in r, maximal margin classifier, support vector classifier etc.
- Instructor explain about cluster analysis, primer in neural networks, decision tree, principal components analysis etc.
Rating:4.5 Out of 5
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#3 Applied Machine Learning For Healthcare – Udemy
Anyone interested to learn the machine learning applications in the healthcare, then you people must take this machine learning applications in the healthcare online course which is created by Eduonix Learning Solutions. Here 400+ pupils are learning this online course. You will understand that how to implement the most popular machine algorithms and you know how to apply the concepts of machine learning topics for the real world scenarios. Here five hours on demanded videos, one article and five downloadable video resources. Here students will enhance their knowledge by building the real world projects on health like breast cancer detection, diabetes prediction, DNA classification, prediction of heart diseases and autism screening.
Key points:
- From the breast cancer detection project you people will learn the topics like breast cancer detection with the KNN and the SVM parts.
- You will learn the topics of diabetes onset detection project like deep learning grid search data set parts, grid search deep learning epochs and batch size, activation, dropout, learning rate, initialization and neurons.
- From the DNA classification datasets section students will understand the concepts such as algorithms and datasets of DNA classification.
- You will understand about the heart disease prediction with the neural networks project and from the autism screening project you will learn about the data processing, neural networks etc.
- Instructors will explain that how to implement the machine learning algorithms and also know how to apply the concepts of machine learning in the real world projects.
Rating: 3.9 Out of 5
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#4 Decision Trees for Machine Learning From Scratch – Udemy
Who are interested in learning the machine learning then you people must take this machine learning online course which is offered by Sefik Ilkin Serengil (software developer). Students will learn that how to build the decision trees for the machine learning from the scratch with the python. You people will learn about the most common algorithms of decision tree. Learn how to develop the code from the scratch. Here this machine learning online course includes one downloadable video resources, assignments and two hours on demanded videos. Instructor will explain the decision trees algorithms fundamentals like C4.5, CART, ID3, regression trees and you can able to design the decision tree frameworks from the scratch in the python. Here 200+ pupils are learning this decision tree for the machine learning online course.
Key points:
- From the ID3 decision tree algorithm section you people will understand the topics like iterative dichotomiser, information gain, overview of ID3 algorithm, ID3 extending and entropy calculations.
- Instructor will cover the concepts of C4.5 decision tree algorithm section like overview of C4.5, gain ratio calculations, C4.5 extending, transforming the decision rules to the python if statements and handling with the continuous features.
- From the CART (classification and regression trees) section you people will understand the concepts such as overview of CART, CART for the classification, know how create the termination rules and overview of regression trees.
- Students will also understand about the random forest and gradient boosting machines introduction.
- You will understand that how to apply all the machine learning topics in the real world projects.
Rating: 4.2 Out of 5
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Conclusion:
Instructors are determined all the required and important concepts of applied machine learning. If you people are interested in learning this applied machine learning course then choose any course from the mention courses. Students can also add this applied machine learning course certificate to the resumes. By learning this applied machine learning course students will get the job opportunities like applied scientist, machine learning scientist, machine learning developer, machine learning engineer, machine learning modeling engineer, research engineer, applied machine learning scientist, data scientist and so on. If you are gratified with this machine learning article then share it with your colleagues, friends through hike, linkedIn, facebook, whatsapp, twitter etc. trainers will surely clear out your doubts.
Best Applied Machine Learning Books:
#1 Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 1st Edition by Aurélien Géron
#2 Machine Learning with R – Second Edition: Expert techniques for predictive modeling to solve all your data analysis problems 2nd Edition by Brett Lantz
#3 Machine Learning for Absolute Beginners: A Plain English Introduction by Oliver Theobald
#4 Python Machine Learning: A Deep Dive Into Python Machine Learning and Deep Learning, Using Tensor Flow And Keras: From Beginner To Advance by Leonard Eddison
#5 Machine Learning (McGraw-Hill International Editions Computer Science Series) 1st Edition by Tom M. Mitchell
#6 Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition 2nd Edition by Sebastian Raschka & Vahid Mirjalil
