Data Science

Deep Learning is the latest new area in machine learning which is proving handy to solve many interesting problems which were not possible to do with the standard ML algorithms before such as Language Translation, Text to speech, Speech Recognition etc. It has moved us one step closer to what we thought of as our objective. Self Driving cars are the latest invention of this field and is growing at a very fast rate with more number of developers opting to move to deep learning. Some of the most interesting projects in deep learning that helps you learn concepts as well as apply these concepts on real world data and complex use cases are: Tranfer Learning : To use a pretrained model to find objects in any image Style Transfer : Applying the style of one image onto the other image Digit Recognizer : Use CNN to find the handwritten digits with our own custom built CNN model MNIST data : Classify over 10 objects in the MNIST data TED Talks : Creating word embeddings in a dataset

Gaining experience from the blogs of all the winners of online competition, I could see what approaches they follow, their solutions etc. Some of the most important points that I try to follow when I see a new machine learning challenge are: 1. Understand : Try to understand the data thoroughly, what each feature means and how it it related to the target variable. Exploring the data using plots gives an idea about features.  2. Data cleaning : Imputation of missing values and data pre-processing.  3. Feature Engineering : The most important part is feature engineering. Good feature engineering can give you an edge over others in any competition. In fact, it's the most important and most difficult step to think of and comes only with experience and practice. 4. Splitting your data:  Creating a strategy how to split your data into training and validation set. A good validation set that matches your score on the leader board is very crucial to fine tune your code. 5. Bench