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Neural network activation functions
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Hypothesis Testing
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Choosing a right parametric test
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Important Pandas & Spark Commands for Data Science
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Bayesian Data Analysis
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Data science/ML/AI
Repost from Programming, data science, ML - free courses by Big Data Specialist
Pandas complete tutorial
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Data science/ML/AI
What is PCA PCA is a commonly used tool in statistics for making complex data more manageable. Here are some essential points to get started with PCA in R: 🔹 What is PCA? PCA transforms a large set of variables into a smaller one that still contains most of the information in the original set. This process is crucial for analyzing data more efficiently. 🔸 Why R? R is a statistical powerhouse, favored for its versatility in data analysis and visualization capabilities. Its comprehensive packages and functions make PCA straightforward and effective. 🔹 Getting Started: Utilize R's prcomp() function to perform PCA. This function is robust, offering a standardized method to carry out PCA with ease, providing you with principal components, variance captured, and more. 🔸 Visualizing PCA Results: With R, you can leverage powerful visualization libraries like ggplot2 and factoextra. Visualize your PCA results through scree plots to decide how many principal components to retain, or use biplots to understand the relationship between variables and components. 🔹 Interpreting Results: The output of PCA in R includes the variance explained by each principal component, helping you understand the significance of each component in your analysis. This is crucial for making informed decisions based on your data. 🔸 Applications: Whether it's in market research, genomics, or any field dealing with large data sets, PCA in R can help you identify patterns, reduce noise, and focus on the variables that truly matter. 🔹 Key Packages: Beyond base R, packages like factoextra offer additional functions for enhanced PCA analysis and visualization, making your data analysis journey smoother and more insightful. Embark on your PCA journey in R and transform vast, complicated data sets into simplified, insightful information. Ready to go from data to insights? Our comprehensive course on PCA in R programming covers everything from the basics to advanced applications.
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Data science/ML/AI
Repost from Python Learning
Python for Data Visualization: The Complete Masterclass Transforming Data into Insights: A Comprehensive Guide to Python-based Data Visualization Rating ⭐️: 4.6 out 5 Students 👨‍🎓 : 29,613 Duration ⏰ : 3.5 hours on-demand video Created by 👨‍🏫: Meta Brains 🔗 Course Link ⚠️ Its free for first 1000 enrollments only! #python #data_visualization ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @bigdataspecialist for more👈
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Python for Data Visualization: The Complete Masterclass

Transforming Data into Insights: A Comprehensive Guide to Python-based Data Visualization

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Data science/ML/AI
Data Science Full Course For Beginners 2024 Fundamentals of Data Science: Understand the basics, including data types, data collection, and data cleaning. Statistics & Probability: Dive into the math that powers data analysis. Data Visualization: Learn to create insightful visual representations of data. Machine Learning: Get hands-on with algorithms and models that make predictions based on data. Tools & Technologies: Master the use of Python, R, SQL, and key data science libraries and frameworks. Real-World Projects: Apply your knowledge on real data science problems and solutions. 🆓 Free Online Course 🎬 video lesson 🏃‍♂️ Self paced Duration ⏰: 6-7 hours worth of material Source: simplilearn 🔗 Course Link #data_science #machinelearning ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @datascience_bds for more👈
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Data Science Full Course For Beginners 2024 | Learn Data Science In 6 Hours | Simplilearn

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Data science/ML/AI
5 Best beginner-friendly data science projects! 1-Loan Approval Prediction 2-Credit Card Fraud Detection 3-Netflix Movies and TV Shows Analysis 4-Sentiment Analysis of Tweets 5-Weather Data Analysis These projects are ideal for beginners who want to grasp the fundamentals and get closer to solving real-life projects. How to choose the right portfolio project? Here are my best tips: Pick What You Like: Choose a topic you enjoy to keep the project fun. Show Your Skills: Make sure your project shows off what you can do, like organizing data or making charts. Keep It Simple: Start with a simple project that you can expand later. Use Available Data: Choose a project with easy-to-find data.
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