Remove Data Wrangling Remove Decision Trees Remove Deep Learning
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Supercharge your skill set with 9 free machine learning courses

Data Science Dojo

Machine Learning with TensorFlow by Google AI This is a beginner-level course that teaches you the basics of machine learning using TensorFlow , a popular machine-learning library. The course covers topics such as linear regression, logistic regression, and decision trees.

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10 best data science bootcamps in 2023

Data Science Dojo

Data science boot camps are intensive, short-term programs that teach students the skills they need to become data scientists. These programs typically cover topics such as data wrangling, statistical inference, machine learning, and Python programming.

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Training Sessions Coming to ODSC APAC 2023

ODSC - Open Data Science

Build Classification and Regression Models with Spark on AWS Suman Debnath | Principal Developer Advocate, Data Engineering | Amazon Web Services This immersive session will cover optimizing PySpark and best practices for Spark MLlib. Finally, you’ll explore how to handle missing values and training and validating your models using PySpark.

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Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

D Data Mining : The process of discovering patterns, insights, and knowledge from large datasets using various techniques such as classification, clustering, and association rule learning. Data Wrangling: The cleaning, transforming, and structuring of raw data into a format suitable for analysis.

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Top 10 Data Science Interviews Questions and Expert Answers

Pickl AI

Machine Learning Algorithms Candidates should demonstrate proficiency in a variety of Machine Learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks. What is the Central Limit Theorem, and why is it important in statistics?

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Big Data Syllabus: A Comprehensive Overview

Pickl AI

Data Cleaning and Transformation Techniques for preprocessing data to ensure quality and consistency, including handling missing values, outliers, and data type conversions. Students should learn about data wrangling and the importance of data quality.