by Vicky Roy
Machine Learning is a multi-disciplinary field that covers many subjects such as mathematics and programming languages. With the help of Machine learning, we can draw the meaningful inferences from previous experiences. Machine Learning can be used any various fields such as finance, data analysis, robotics, Marketing, Healthcare and sales, Transportation etc,.
Many companies such as Facebook, Google, Amazon, Microsoft, Intel, Wix, Quora, etc,. have already indulged the machine learning algorithms to improve the quality of services. Facebook uses Face detection, Friend suggestions algorithms etc,. Google uses machine learning algorithms for video recommendations(YouTube), online advertising(AdSense, Real-time Bidding), Search engines. Quora uses machine learning for showing personalized feed based on their user’s interest.
The demand for the Machine learning has increased over the past few years and companies are looking for the employees who have skills in Machine Learning and Data Science. There are many resources to learn Machine learning but the below resources helps to master Machine Learning.
To learn machine learning, you need to have prior knowledge of mathematical concepts such as Calculus, Linear, Algebra, Probability, Statistics, And also you need knowledge of any programming language Python, R, or Matlab.
This tutorial gives you a comprehensive overview of the machine learning, data Science, and deep learning. In the tutorial, the instructor gives a basic introduction of Python programming, Statistics, and probability and then covers topics of data mining, Artificial intelligence, machine learning such as Bayesian theorem, regression analysis, K-means Clustering, principle component analysis, decision and much more,. The instructor also shows how to create the Artificial Neural Networks with Tensorflow and Keras. Here, you will not only the learn theory but you will also learn how to create the recommender system, spam classifier, search engine, Handwriting recognization, sentimental analysis practically. The instructor keeps motivating and explains the concepts without any complexity. This video tutorial is best suitable for the beginners.
This tutorial is presented by Google Developers and Josh, the instructor of this tutorial walks you through concepts of the Machine learning with the help of the popular libraries called Tenserflow and Scikit-Learn. He explains how to install Anaconda and use of classifiers under supervised learning. He also explains the decision trees, k-Nearest Neighbors etc,. I would recommend this without a miss. This video tutorial is completely for the beginners and who already have knowledge of Python programming.
This video tutorial is designed for both beginners and data scientists. I would say this video tutorial will be helpful to the job seekers and data scientists who want to build their career in machine learning. In this video tutorial, you will learn the use of Python for machine learning and Data science. You will able to learn how to implement the machine learning algorithms.
This tutorial is presented by Sentex that is popular Youtube channel for learning Python-based programming. Here you can also find tutorials on Python programming for web development, Machine learning, finance, data analysis, robotics, web development, game development and more. In this video tutorial, the instructor walks you through the Machine learning concepts such as Regression, classification, support vector machines, clustering, deep learning and tenser flow neural networks, convolutional neural network etc,. This tutorial is best suitable for the beginners and intermediate.
This tutorial mainly focuses on explaining how Neural Networks are used for Machine learning in speech, object recognization and image segmentation, modeling language, human motion etc,. Geoffrey walks you through the machine learning algorithms and how these algorithms help us in achieving the speech, object recognization and image segmentation, human motion.
This tutorial primarily focuses on explaining how to use Scikit-learn library for Machine Learning. Here the instructor begins with a quick introduction of machine learning and how it is used, then he walks you through how to set up an environment for working with Scikit-learn library, the instructor shows how to install Anaconda and configures the IPython interpreter. He also concepts such as training the models, comparing the models, pandas, Seaborn, selecting the best models, how to evaluate the classifier etc,. It is worth to go through this tutorial. This tutorial is best suitable for the intermediates.
Happy Learning 🙂