Read basics of ML and DL

[varunp04]

go through all the theory of the ML and DL

go thru these links:

1. 100-Days-Of-ML-Code

   • 𝐋𝐢𝐧𝐤: (https://lnkd.in/dcftdA57)
   • 𝐒𝐭𝐚𝐫𝐬: ~42k

2. awesome-datascience

   • 𝐋𝐢𝐧𝐤: (https://lnkd.in/dcFYYwx9)
   • 𝐒𝐭𝐚𝐫𝐬: ~22.7k

3. Data-Science-For-Beginners

   • 𝐋𝐢𝐧𝐤: (https://lnkd.in/d_zZBadF)
   • 𝐒𝐭𝐚𝐫𝐬: ~14.5k

4. data-science-interviews

   • 𝐋𝐢𝐧𝐤: (https://lnkd.in/dkN4RZjH)
   • 𝐒𝐭𝐚𝐫𝐬: ~5.8k

5. Coding and ML System Design

   • 𝐋𝐢𝐧𝐤: (https://lnkd.in/gXFaaaQR)
   • 𝐒𝐭𝐚𝐫𝐬: ~3.5k

6. Machine Learning Interviews from MAANG

   • 𝐋𝐢𝐧𝐤: https://lnkd.in/gq_huuZD
   • 𝐒𝐭𝐚𝐫𝐬: 8.1k

7. data-science-ipython-notebooks

   • 𝐋𝐢𝐧𝐤: (https://lnkd.in/dPmQuPB9)
   • 𝐒𝐭𝐚𝐫𝐬: ~27.2k

[varunp04]

practice two projects: #1 #2

[varunp04]

Go through the basics of ML models (end date: 31st Oct)

• [x] Linear regression
• [x] logistic regression
• [x] regularization
• [x] feature selection
• [x] decision tree
• [x] random forest
• [x] gradient boosting

[varunp04]

go thru the basics of DL models (9th Nov):

• [x] back propogation
• [x] neural networks
• [ ] LSTM
• [ ] CNN
• [ ] RNN
• [ ] Auto encoders
• [ ] GRUs

[varunp04]

kaggle exercise for Time series model Link: