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Artificial intelligence with Python 04 months course begins Dec 15
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By popular demand part II of #AIWithPython #BlackInRoboticsWorkshop has just been added to the schedule. Facilitated by @spaceCadetShep on 11/19/23, sign ups start tomorrow, only $5. Come get your robot blessing.
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Day 22
After A good Calculus Essentials rundown into course, I heard to see how these Maths are applied in FeedForwarding within the Neural Networks. This enables me understand, the basic Neural Network Terminologies too.
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Day 21
Augmenting my learning from @udacity with other platforms such as @DataCamp and @kaggle keeps the quest for mastering Machine Learning and Data science as a whole more interesting. Today was for matplotlib review on Datacamp.
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Day 15
From learning complex Linear algebra Models, i aced up to the grasping the fundamentals of Calculus in Machine Learning.
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Day 13
Working with Qualitative and Quantitative Data led me on a journey to working with Scatterplots, Dealing with Overplotting, Transparency and Jitter.
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Day 12
I looked at interpreting discrete data using 2-Dimensional Heat Maps.
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Day 11
Implementing Matplotlib's functionalities, enabled me access her Violin and Box visualization graphs for more discrete data. This way I'm able to clean my discrete data for better interpretation.
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Day 10
I implemented seaborn libraries in the visualization of my data. I used the seaborn color palette and more.
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Day 09
Working with data sets will also involve sorting out possible empty sets. An example is finding NaNs, modifying them to avoid error during ML operations. Today, I worked on NaN sorting using the .count, .fillna and many more attributes.
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Day 06
I commenced work on Numpy libraries. Numpy datatypes and functions. I tested and compared Numpy arrays to Python lists. I used the zeroes, random, radint functions etc.
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Day 03
Today I worked on the various techniques of Importing Modules in Python. I looked at the various Python packages in the Standard Python Library and Third Party libraries. I also commenced Object Oriented Programming basics; Classes, Attributes and Methods.
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