Fake news Detection Machine Learning Project with 92%Accuracy
it contain compressed file in which "jupyter notebook file and dataset"✅

🎲Dice_roll_Simulator_Gui with python in 2 minute 😊

Pandas

Data_science Numpy cheat sheet

Dimensionality reduction techniques

Singular Value Decomposition (SVD)
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
T-distributed Stochastic Neighbor Embedding (t-SNE)
Autoencoders
Fourier and Wavelet Transforms

What is the curse of dimensionality? Why do we care about it?

Data in only one dimension is relatively tightly packed. Adding a dimension stretches the points across that dimension, pushing them further apart. Additional dimensions spread the data even further making high dimensional data extremely sparse. We care about it, because it is difficult to use machine learning in sparse spaces.

K-means vs DBScan ML Algorithm

DBScan is more robust to noise.
DBScan is better when the amount of clusters is difficult to guess.
K-means has a lower complexity, i.e. it will be much faster, especially with a larger amount of points.

Start working on any project if you are a beginner and want to grow your career as a data scientist
You will learn much more as you practice and work on projects from yourself
You can find dataset in this channel or go to kaggle to find any random dataset and just work on it
Learning concepts is fine but most of the learnings come from projects
I know that might feel boring at first time but as you move forward, it become interesting

👉The Ultimate Guide to the Pandas Library for Data Science in Python
👇👇

https://www.freecodecamp.org/news/the-ultimate-guide-to-the-pandas-library-for-data-science-in-python/amp/

A Visual Intro to NumPy and Data Representation
.
Link : 👇👇
https://jalammar.github.io/visual-numpy/

Matplotlib Cheatsheet 👇👇

https://github.com/rougier/matplotlib-cheatsheet

SQL Cheatsheet 👇👇

https://websitesetup.org/sql-cheat-sheet/

Seeing Theory : A visual introduction to probability and statistics

Link :👇👇
https://seeing-theory.brown.edu/

“The Projects You Should Do to Get a Data Science Job” by Ken Jee
👇👇
https://link.medium.com/Q2DnxSGRO6

👉A handy notebook on handling missing values

Link : 👇👇
https://www.kaggle.com/parulpandey/a-guide-to-handling-missing-values-in-python

A list of NLP Tutorials

Link : 👇👇
https://github.com/lyeoni/nlp-tutorial


“An Implementation and Explanation of the Random Forest in Python” by Will Koehrsen 👇👇
https://link.medium.com/GCWFv81v95

“How to analyse 100s of GBs of data on your laptop with Python” by Jovan Veljanoski 👇👇
https://link.medium.com/V8xS82Cax6

Skills Required For A Data Analyst in 2021

Basic Excel- 
https://www.youtube.com/playlist?list=PLmQAMKHKeLZ_ADx6nJcoTM5t2S1bmsMdm

Advanced Excel- 
https://www.youtube.com/playlist?list=PLmQAMKHKeLZ_e9xmZNPACsLdgie3Tkaxf

SQL-
https://www.youtube.com/watch?v=5JCyiutyu_o&list=PLmQAMKHKeLZ-kD9VN0prfKCByr9pa4jw6

SQL- (Khan Academy)-
https://www.khanacademy.org/computing/computer-programming/sql

Python Programming Language- 
https://www.youtube.com/watch?v=bPrmA1SEN2k&list=PLZoTAELRMXVNUL99R4bDlVYsncUNvwUBB

Stats Lectures- 
https://www.youtube.com/watch?v=zRUliXuwJCQ&list=PLZoTAELRMXVMhVyr3Ri9IQ-t5QPBtxzJO

Stats Lectures(Khans Academy)-
https://www.khanacademy.org/math/statistics-probability

Python EDA- 
https://www.youtube.com/playlist?list=PLZoTAELRMXVPQyArDHyQVjQxjj_YmEuO9

Python Feature Engineering- 
https://www.youtube.com/playlist?list=PLZoTAELRMXVPwYGE2PXD3x0bfKnR0cJjN

Tableau- 
https://www.tableau.com/academic/student-Iron-Viz

Here is the explanation for the quiz

The F-beta score is the weighted harmonic mean of precision and recall, reaching its optimal value at 1 and its worst value at 0. The highest possible value of an F-score is 1.0, indicating perfect precision and recall, and the lowest possible value is 0, if either the precision or the recall is zero. The F-score is commonly used for evaluating information retrieval systems such as search engines, and also for many kinds of machine learning models, in particular in natural language processing.

Amazing response from you guys in this poll

Lets start with project #1

Fake News Detection

This is an example of text classification since we need to classify whether a news is real or fake

You can refer dataset from kaggle to work on such an amazing project
https://bit.ly/3FGcyoJ
Or
https://www.kaggle.com/c/fake-news/data

Before you work on this project, you should have fair understanding of below topics

Concepts: Stopwords, Porter Stemmer, Tokenisation, Tfid Vectorizer, LSTM, NLP

Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, re, nltk, Tensorflow

Steps:
1. Go through dataset
2. Import the libraries
3. Exploratory Data Analysis [EDA]
4. Data Visualization
5. Data Preparation using Tokenisation and padding
6. Apply theoretical concepts to reduce unnecessary words using Stopwords and Porter Stemmer. Convert text to vector using Count Vectorizer.
7. Split dataset into training and testing
8. Build and train the model using ML Algorithms
9. Model Evaluation using accuracy, recall, precision, confusion matrix and other metrics concepts

Algorithms you can apply:
Logistic Regression, Support Vector Machine, Multilayer Perceptron, KNN, Random Forest, Linear SVM, etc.

ENJOY LEARNING 👍👍