A z datasciencepython courseresources

Greetings, how are you doing? I hope it’s great.
Due to high volume of request for new ways to understand and adopt machine learning, deep
learning and other Ai concepts in the space of analytics We decided to put the course in the text
format provided with the references along with the codes to cope up the speed of adopting
innovation and make it accessible straight back to you.
The reference links contains premium shared links exclusively for this course, accessing those in
general requires subscription to the hosting platform Medium. Also provided with Python & R codes
for ease of code reusability and learning. Don’t forget to subscribe and support. Enjoy………
1.) 5 Types Regression in 45 lines of code
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2.) 7 Types of Classification using python
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3.) Let’s Develop Artificial Neural Network in 30 lines of code — II
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4.) Let’s Develop Artificial Neural Network in 30 lines of code
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5.) Reinforcement Learning | In 31 Steps
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6.) What is PCA and How can we apply Real Quick and Easy Way?
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7.) What is Supervised Linear Discriminant Analysis(LDA) ~ PCA
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8.) What is Kernel PCA? using R & Python
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9.) Association Rule Learning
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10.) Multi-Layer Perception Time Series
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11.) LSTMs for regression
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12.) Uni-Variate LSTM Time Series Forecasting
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13.) Multi-variate LSTM Time Series Forecasting.
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14.) Multi-Step LSTM Time Series Forecasting


/>15.) Grid Search For ML & Deep Learning Models
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16.) 7 types of Multi*-Classification using python
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