Hi All
Welcome to GeekyCodes. Thank you for 500 members in this group. Join our channel for latest Programming Blogs,Job Openings at various organizations and machine learning blogs.
In case you've any doubt regarding ML/Data Science please reach out to me @ved1104
Welcome to GeekyCodes. Thank you for 500 members in this group. Join our channel for latest Programming Blogs,Job Openings at various organizations and machine learning blogs.
In case you've any doubt regarding ML/Data Science please reach out to me @ved1104
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Why we can never fully optimize
ML models
ML models
How to better prepare for ML roles?
[Assuming you have relavant ML projects]
If your resume get selected by a team, it means your skills are relavant.
So How do you make sure to increase your chance of selection?
Divide your preparation in this way.
1. Prepare your pojects well:
a. Prepare an overview of the project having problems statement and solution in short.
b. Prepare the working of algorithms you have used, data preprocessing techniques etc.
c. Prepare in detail about Evaluation Metric and other metric you used to evaluate this model and performance.
d. Prepare deployment and training related details.
2. Prepare general ML questions: Study classical ML techniques like regression, logistic, clustering, tree-based algorithm, bias-vairance tradeoff, handling missing data, overfitting and underfitting, common evaluation metrics.
3. If you are applying to deep learning based roles:
a. Revise AN, CNN, RNN, basics of transformer etc.
b. Read topics like gradient descent, backpropagation, normalization, dropout, and activation function, which are common across DL.
c. Be ready with questions based on DL architecture.
4. Be good at coding problems.
a. Prepare basics of Data Structure and Algorithm
b. Prepare Sorting, Searching algorithms well
I think these are enough for increasing your chance to clear any ML based interview.
Overall, interviewer checks you coding skills, your ML/DL understanding, and your ability to work on newer problems.
Also, mathematics and statistics are part of interview questions too. So, keep these things in loop.
Keep growing.
[Assuming you have relavant ML projects]
If your resume get selected by a team, it means your skills are relavant.
So How do you make sure to increase your chance of selection?
Divide your preparation in this way.
1. Prepare your pojects well:
a. Prepare an overview of the project having problems statement and solution in short.
b. Prepare the working of algorithms you have used, data preprocessing techniques etc.
c. Prepare in detail about Evaluation Metric and other metric you used to evaluate this model and performance.
d. Prepare deployment and training related details.
2. Prepare general ML questions: Study classical ML techniques like regression, logistic, clustering, tree-based algorithm, bias-vairance tradeoff, handling missing data, overfitting and underfitting, common evaluation metrics.
3. If you are applying to deep learning based roles:
a. Revise AN, CNN, RNN, basics of transformer etc.
b. Read topics like gradient descent, backpropagation, normalization, dropout, and activation function, which are common across DL.
c. Be ready with questions based on DL architecture.
4. Be good at coding problems.
a. Prepare basics of Data Structure and Algorithm
b. Prepare Sorting, Searching algorithms well
I think these are enough for increasing your chance to clear any ML based interview.
Overall, interviewer checks you coding skills, your ML/DL understanding, and your ability to work on newer problems.
Also, mathematics and statistics are part of interview questions too. So, keep these things in loop.
Keep growing.
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