
Kick off the practical machine learning course by outlining 30 hands-on projects and learning goals, setting expectations for applying ML techniques across real-world tasks.
Master data preprocessing and feature engineering by handling nulls, encoding categoricals, dropping useless columns, and creating features like rent per person and per bedroom to improve model accuracy.
Apply principal component analysis for linear dimensionality reduction, compare PCA before/after with decision tree, random forest, and MLP classifiers, using label encoding and cross-validated accuracy to predict stroke.
This lecture introduces loan approval prediction project, importing pandas and seaborn, exploring a 614-row dataset of 13 features including loan status, and preparing to convert categorical columns to numerical features.
Explore data preprocessing and feature engineering with pipelines that separate numerical and categorical features, drop non-generalizable columns, and compare random forest, decision tree, and logistic regression through cross-validation.
In the machine learning practical course, build and evaluate regression models from linear regression to boosting and ensemble regressors. Clean data, analyze correlations, and compare model performance to improve accuracy.
Machine learning has inserted itself into the fiber of our everyday lives – even without us noticing. Machine learning algorithms have been powering the world around us, and this includes product recommendations at Walmart, fraud detection at various top-notch financial institutions, surge pricing at Uber, as well as content used by LinkedIn, Facebook, Instagram, and Twitter on users’ feeds, and these are just a few examples, grounded directly in the daily lives we live.
This being said, it goes without saying that the future is already here – and machine learning plays a significant role in the way our contemporary imagination visualises it. Mark Cuban, for instance, has said: “Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise you’re going to be a dinosaur within 3 years.”
Machine learning makes a mockery of anything that can be called “important” – both at a financial as well as a global scale. If you are looking to take your career to another level, Machine Learning can do that for you. If you are looking to involve yourself in something that will make you part of something that is global as well as contemporary relevance, Machine Learning can do that for you as well.
Machine learning covers significant ground in various verticals – including image recognition, medicine, cyber security, facial recognition, and more. As an increasing amount of businesses are realising that business intelligence is profoundly impacted by machine learning, and thus are choosing to invest in it.
Netflix, to take just one example, announced a prize worth $1 million to the first person who could sharpen its ML algorithm by increasing its accuracy by 10%. This is sureshot evidence that even a slight enhancement in ML algorithms is immensely profitable for the companies that use them, and thus, so are the people behind them. And with ML, you can be one of them!
The best machine learning engineers these days are paid as much as immensely popular sports personalities! And that’s no exaggeration! According to Glassdoor, the average machine learning engineer salary is 8 lakhs per annum – and that’s just at the starting of one’s career! An experienced machine learning engineer takes home anywhere between 15 to 23 lakhs per annum.