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*** Computer Science. Computational Thinking. Problem Solving: Inputs, Outputs. Representation: Unary, Binary, Decimal, ASCII, Unicode, RGB. Abstraction. Algorithms. Running Times. Pseudocode. Scratch: Functions, Arguments, Return Values; Variables; Boolean Expressions, Conditionals; Loops; Events; Threads. *** TABLE OF CONTENTS 00:00:00 ...
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Ready to take your ML skills to the next level? Here’s how you can deepen your knowledge and specialize in the field: 1️⃣ Start with: CS50: Introduction to Computer Science. ( https://www.edx.org/cs50 ) 2️⃣ Master Python Basics: 30 Days of Python. ( https://github.com/Asabeneh/30-Days-Of-Python/tree/master ) 3️⃣ Get Familiar with ML Tools: NumPy, Pandas, Matplotlib. 4️⃣ Follow a Structured ML Cours: Machine Learning Specialization by Andrew Ng. ( https://www.coursera.org/specializations/machine-
Ready to take your ML skills to the next level? Here’s how you can deepen your knowledge and specialize in the field: 1️⃣ Start with: CS50: Introduction to Computer Science. ( https://www.edx.org/cs50 ) 2️⃣ Master Python Basics: 30 Days of Python. ( https://github.com/Asabeneh/30-Days-Of-Python/tree/master ) 3️⃣ Get Familiar with ML Tools: NumPy, Pandas, Matplotlib. 4️⃣ Follow a Structured ML Cours: Machine Learning Specialization by Andrew Ng. ( https://www.coursera.org/specializations/machine-
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