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Project: #FakeNews Detector Using #Python and #ML. Models: DecisionTree Classifiers. Logistic Regression Model. Random Forest Classification ID: FE/23/68871350 #3MTTLearningCommunity #My3MTT 3MTT Nigeria Github Repo to project:

Yusuf Nasir Ahmad

1,408 subscribers

13,193 views • 2 years ago •via X (Twitter)

News & Politics #FakeNews #Python #ML #3MTTLearningCommunity #My3MTT

Anya Rossi• Live Now

Private livecam show

10 Comments

Shamsuddeen Jibril Bagaldi2 years ago

@3MTTNigeria I wish you all the best sir

Yusuf Nasir Ahmad2 years ago

@3MTTNigeria Thank you so much Drone Master

Th3Cl0udGuy ☁️💚🌙2 years ago

@3MTTNigeria May Almighty Allah put Barakah in it brother (ameen)

Yusuf Nasir Ahmad2 years ago

@3MTTNigeria Cyber Security, check the work on Github and check how Fake News breed security threats in Nigeria. I am open for Colab sir.

Yusuf Hamisu2 years ago

@3MTTNigeria Best wishes, Nasiru

Yusuf Nasir Ahmad2 years ago

@3MTTNigeria Thanks so much Mr. Opportunity. Kindly have time to review the Project on Github.

Abdullahi Salihu2 years ago

@3MTTNigeria Nice one Nasir, keep it up

Yusuf Nasir Ahmad2 years ago

@3MTTNigeria Dr. I hope you will get to patronize the project and build on it. It is available on Github.

GieDee2 years ago

@3MTTNigeria More power to your elbow Champ!

Yusuf Nasir Ahmad2 years ago

@3MTTNigeria Thanks Boss

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Sadiq 🇳🇬🇵🇸

34,715 views • 1 year ago

if you're struggling on where to start learning ML, here’s a playlist of 30 youtube videos to learn machine learning fundamentals from scratch "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

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ℏεsam

108,861 views • 1 year ago

a playlist of 30 youtube videos to learn machine learning fundamentals from scratch if you're struggling on where to start learning ML, this list goes this "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

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ℏεsam

117,570 views • 1 year ago

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40,230 views • 1 year ago

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