what are some macine learning algorithms that you should always have a strong understanding of and

this airtcle is wrote by  Sean Owen,Director ,Data Science @Cloudera

I think one needs to have a competent knowledge of 1-2 techniques in:

• Regression
• Classification
• Clustering
• Collaborative filtering
• (Bonus) Inference via graphical models

Certainly, it's valuable and important to understand simple Linear regression.

Gradient descent is important because it underpins common classifier techniques like Logistic regression. Also: the Support vector machine. 
 
I also strongly encourage people to have a working knowledge of Random forest classification / regression. It's inherently an ensemble method, effective, and has different properties from the above.

K-means++ clustering is a must.

For collaborative filtering, neighborhood methods are simple enough that almost don't deserve mention. I would try to understand latent factor models based on low-rank matrix factorization like the Singular value decomposition  or simple alternating least squares (http://yifanhu.net/PUB/cf.pdf)

Bonus: MCMC methods (Markov chain Monte Carlo) for graphical models.