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Working in a hands-on learning environment, led by our Data Science Algorithms expert instructor, students will learn about and explore: Get to know seven algorithms for your data science needs in this concise, insightful guide. Ensure you’re confident in the basics by learning when and where to use various data science algorithms. Learn to use machine learning algorithms in a period of just 7 days.

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January 6, 2021

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Course Description

Machine learning applications are highly automated and self-modifying, and they continue to improve over time with minimal human intervention as they learn with more data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed that solve these problems perfectly. Data science helps you gain new knowledge from existing data through algorithmic and statistical analysis. This course will address the problems related to accurate and efficient data classification and prediction. Over the course of 7 days, you will be introduced to seven algorithms, along with exercises that will help you learn different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. You will then find out how to predict data based on the existing trends in your datasets. This course covers algorithms such as: k-Nearest Neighbors, Naive Bayes, Decision Trees, Random Forest, k-Means, Regression, and Time-series. On completion of the book, you will understand which machine learning algorithm to pick for clustering, classification, or regression and which is best suited for your problem.

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Course Curriculum

    • Classification Using K Nearest Neighbors 00:00:00
    • Mary and her temperature preferences 00:00:00
    • Implementation of k-nearest neighbors algorithm 00:00:00
    • Map of Italy example – choosing the value of k 00:00:00
    • House ownership – data rescaling 00:00:00
    • Text classification – using non-Euclidean distances 00:00:00
    • Text classification – k-NN in higher-dimensions 00:00:00
    • Naive Bayes 00:00:00
    • Medical test – basic application of Bayes’ theorem 00:00:00
    • Proof of Bayes’ theorem and its extension 00:00:00
    • Playing chess – independent events 00:00:00
    • Implementation of naive Bayes classifier 00:00:00
    • Playing chess – dependent events 00:00:00
    • Gender classification – Bayes for continuous random variables 00:00:00
    • Decision Trees 00:00:00
    • Swim preference – representing data with decision tree 00:00:00
    • Information theory 00:00:00
    • ID3 algorithm – decision tree construction 00:00:00
    • Classifying with a decision tree 00:00:00
    • Playing chess – analysis with decision tree 00:00:00
    • Going shopping – dealing with data inconsistency 00:00:00
    • Random Forest 00:00:00
    • Overview of random forest algorithm 00:00:00
    • Swim preference – analysis with random forest 00:00:00
    • Implementation of random forest algorithm 00:00:00
    • Playing chess example 00:00:00
    • Going shopping – overcoming data inconsistency with randomness and measuring the level of confidence 00:00:00
    • Clustering into K Clusters 00:00:00
    • Household incomes – clustering into k clusters 00:00:00
    • Gender classification – clustering to classify 00:00:00
    • Implementation of the k-means clustering algorithm 00:00:00
    • House ownership – choosing the number of clusters 00:00:00
    • Document clustering – understanding the number of clusters k in a semantic context 00:00:00
    • Regression 00:00:00
    • Fahrenheit and Celsius conversion – linear regression on perfect data 00:00:00
    • Weight prediction from height – linear regression on real-world data 00:00:00
    • Gradient descent algorithm and its implementation 00:00:00
    • Flight time duration prediction from distance 00:00:00
    • Ballistic flight analysis – non-linear model 00:00:00
    • Time Series Analysis 00:00:00
    • Business profit – analysis of the trend 00:00:00
    • Electronics shop’s sales – analysis of seasonality 00:00:00
    • Statistics 00:00:00
    • Basic concepts 00:00:00
    • Bayesian Inference 00:00:00
    • Distributions 00:00:00
    • Cross-validation 00:00:00
    • A/B Testing 00:00:00
    • R Reference 00:00:00
    • Introduction 00:00:00
    • Data types 00:00:00
    • Linear regression 00:00:00
    • Python Reference 00:00:00
    • Introduction 00:00:00
    • Data types 00:00:00
    • Flow control 00:00:00
    • Glossary of Algorithms and Methods in Data Science 00:00:00

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