Search result for Algorithm analysis and design Online Courses & Certifications
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Algorithms, Part II
by Robert Sedgewick , Kevin Wayne- 4.9
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Approx. 63 hours to complete
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms. Greedy Algorithm Kruskal's Algorithm...
Regression Machine Learning with Python
by Diego Fernandez- 3.5
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6 hours on-demand video
Train algorithm for mapping optimal relationship between target and predictor features. Then, you’ll define algorithm features by creating target and predictor variables for supervised regression learning task. For algorithm training, you’ll use only relevant predictor features subset or transformations through principal component analysis procedure and linear regression coefficients regularization optimal parameter estimation or fine tuning through time series cross-validation....
$12.99
Digital Signal Processing (DSP) From Ground Up™ in C
by Israel Gbati- 4
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7.5 hours on-demand video
Be able to develop the Inverse Discrete Fourier Transform (IDFT) algorithm in C Be able to perform spectral analysis on ECG signals in C Be able to design and develop Windowed-Sinc filters in C Be able to design and develop Finite Impulse Response (FIR) filters in C Be able to design and develop Infinite Impulse Response (IIR) filters in C...
$14.99
Analysis for Business Systems
by Ken Reily- 4.8
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Approx. 9 hours to complete
Most often, organizations acquire information systems as part of a larger focus on process improvement and efficiency. These organizations need to invest in the right system to meet their needs: right functionality, right size, and for the right price. 3-2 Feasibility Analysis Analysis Phase Documentation Module 4: Design Phase Overview Quiz...
Recommender Systems: Evaluation and Metrics
by Michael D. Ekstrand , Joseph A Konstan- 4.4
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Approx. 7 hours to complete
You will gain familiarity with several families of metrics, including ones to measure prediction accuracy, rank accuracy, decision-support, and other factors such as diversity, product coverage, and serendipity. Basic Prediction and Recommendation Metrics Basic Prediction and Recommendation Metrics Assignment Advanced Metrics and Offline Evaluation Additional Item and List-Based Metrics Usage Logs and Analysis...
Experimental Design Basics
by Douglas C. Montgomery- 4.7
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Approx. 13 hours to complete
This is a basic course in designing experiments and analyzing the resulting data. The course objective is to learn how to plan, design and conduct experiments efficiently and effectively, and analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. Unit 1: Getting Started and Introduction to Design and Analysis of Experiments...
Roadmap to Success in Digital Manufacturing & Design
by Amy Moore- 4.7
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Approx. 18 hours to complete
Learners will create a roadmap to achieve their own personal goals related to the digital manufacturing and design (DM&D) profession, which will help them leverage relevant opportunities. Self-Assessment: Strength, Weaknesses, Opportunities, and Threats (SWOT) Analysis Your Future in Digital Manufacturing and Design Resources: Self-Assessment: Strength, Weaknesses, Opportunities, and Threats (SWOT) Analysis...
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Natural Language Processing with Classification and Vector Spaces
by Younes Bensouda Mourri , Łukasz Kaiser , Eddy Shyu- 4.6
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Approx. 31 hours to complete
a) Perform sentiment analysis of tweets using logistic regression and then naïve Bayes, c) Write a simple English to French translation algorithm using pre-computed word embeddings and locality sensitive hashing to relate words via approximate k-nearest neighbor search. Sentiment Analysis with Logistic Regression Supervised ML & Sentiment Analysis Negative and Positive Frequencies...
Deep Learning Regression with Python
by Diego Fernandez- 3.4
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4 hours on-demand video
Regularize algorithm learning through nodes connections weight decay, visible or hidden layers dropout fractions and stochastic gradient descent algorithm learning rate. Then, you’ll define algorithm features by creating target and predictor variables for supervised regression learning task. For algorithm training, you’ll use only relevant predictor features subset or transformations through principal components analysis procedure and nodes connections weight decay regularization....
$9.99
Factorial and Fractional Factorial Designs
by Douglas C. Montgomery- 4.8
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Approx. 12 hours to complete
As the number of factors of interest grows full factorials become too expensive and fractional versions of the factorial design are useful. Analysis Procedure for a Factorial Design The 2^k design and design optimality Unit 3: Blocking and Confounding in the 2^k Factorial Design Conduct a factorial experiment in blocks and construct and analyze a fractional factorial design...
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