Machine learning algorithm basics
Total of 79 lessons20 hours and 14 minutes and 25 seconds
Total of 61 lessons1 days and 3 hours and 16 minutes and 34 seconds
The "Big Data Machine Learning" course is a basic theoretical course for senior undergraduates or graduate students in the information discipline. The purpose is to cultivate students to have an in-depth understanding of the theoretical basis of big data machine learning, a firm grasp of big data machine learning methods, and the ability to solve practical problems. and other comprehensive abilities. The main contents of the course include: basic theories of statistical learning, basic methods of machine learning, and deep learning theories and methods.
Total of 113 lessons15 hours and 39 minutes and 33 seconds
Artificial intelligence is the hottest technology today. All major manufacturers want to make their own artificial intelligence products, but the entry threshold for artificial intelligence is very high. It is not easy to quickly make artificial intelligence products. This course will introduce you to it. This open source robot language recognition framework allows everyone to quickly implement their own artificial intelligence products and implement customized functions such as speech recognition, semantic understanding, and speech output.
Total of 15 lessons3 hours and 1 minutes and 6 seconds
Statistical learning is a science about probabilistic statistical models built by computers based on data and using the models to predict and analyze data. Statistical learning has also become statistical machine learning.
Total of 41 lessons1 days and 47 minutes and 24 seconds
Machine learning technology is the preferred solution for realizing artificial intelligence. In this seminar, we introduce the many advantages of NXP's i.MX RT crossover processor in supporting machine learning. These advantages make it logical and easy to introduce machine learning locally on the node. Subsequently, based on examples of object recognition and keyword detection, we will also demonstrate in this seminar the entire process of deploying and running a machine learning model on i.MX RT, as well as the integration of the machine learning module with the overall system.
Total of 2 lessons35 minutes and 0 seconds
The goal of machine learning is to program a computer to solve a given problem using sample data or past experience. There have been many successful applications of machine learning, including analyzing past sales data to predict customer behavior, face recognition or speech recognition, optimizing robot behavior to use the least amount of resources to complete a task, and various systems for extracting knowledge from biometric data. . In order to provide a unified discussion on machine learning problem solving, "Introduction to Machine Learning" discusses machine learning in statistics, pattern recognition, and neural networks. Applications in different fields such as artificial intelligence, signal processing, control and data mining.
Total of 42 lessons1 days and 4 hours and 6 minutes and 25 seconds
Introduces basic algorithms, theories and practical tools that machine learning users in various fields should know.
Total of 65 lessons15 hours and 29 minutes and 53 seconds
(01) Preliminary machine learning and related mathematics (02) Mathematical statistics and parameter estimation (03) Matrix analysis and application (04) Preliminary convex optimization (05) Regression analysis and engineering application (06) Feature engineering (07) Workflow and model Tuning (08) Maximum entropy model and EM algorithm (09) Recommended system and application (10) Clustering algorithm and application (11) Decision tree random forest and adaboost (12) SVM (13) Bayesian method (14) Topic Model (15) Bayesian inference sampling and variation (16) Artificial neural network (17) Convolutional neural network (18) Recurrent neural network and LSTM (19) Caffe&Tensor Flow&MxNet Introduction (20) Bayesian network and HMM (extra Supplement) word embedding word embedding
Total of 21 lessons1 days and 22 hours and 12 minutes and 36 seconds
(1) Understand machine learning and introduce classic algorithms by introducing the basic problems of machine learning (classification, clustering, regression, dimensionality reduction); (2) Python third-party library sklearn (scikit-learn), explain how to apply machine learning algorithms to quickly solve problems approach to practical problems.
Total of 27 lessons3 hours and 17 minutes and 52 seconds
Total of 1 lessons1 minutes and 46 seconds
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