Did you know Python is the lingua franca of machine learning? You can't avoid learning Python if you work on machine learning problems. You need to know what other people's code means and you need to convey your ideas to them too. Once you have learned a bit of basic Python, boost your skills on a fast track with Machine Learning Mastery's new book: Not having sufficient Python skills is a problem. You will get lost when you see your Python code doesn't work as expected, and you have no clue as to why.

Hi, I'm Jason Brownlee PhD and I help developers like you skip years ahead. Discover how to get better results, faster. Click the button below to get my free EBook and accelerate your next project (and access to my exclusive email course). Send Me the Free eBook! Join over 150,000 practitioners who already have a head start.

There are standard workflows in a machine learning project that can be automated. In Python scikit-learn, Pipelines help to to clearly define and automate these workflows. In this post you will discover Pipelines in scikit-learn and how you can automate common machine learning workflows. Automate Machine Learning Workflows with Pipelines in Python and scikit-learn Photo by Brian Cantoni, some rights reserved. There are standard workflows in applied machine learning.

"I did not have lines in the resume that showed my ML expertise. I did not have a Data Science industry experience or relevant papers. For this week's ML practitioner's series, Analytics India Magazine got in touch with Vladimir Iglovikov, an ex-Spetsnaz, theoretical physicist and also a Kaggle GrandMaster. In this exclusive interview, he shares valuable information from his journey in the world of data science. After a brief stint in Russian special forces, Iglovikov enrolled for the Master's programme in theoretical Physics at the St.Petersburg State University whose distinguished alumni include President Vladimir Putin. In September 2010, Iglovikov moved to California to pursue a PhD in Physics from UC Davis and on completion of the degree, he moved to Silicon Valley in the summer of 2015. Currently, Iglovikov works as Sr. Software Engineer at Lyft, a ride-sharing company that operates in the United States and Canada. His work is centered around building robust machine learning models for autonomous vehicles at Lyft, Level5. Post PhD, Iglovikov had two options in hand. One was to pursue postdoc, and the other was to get into the industry as a software engineer. His career took a new turn when one of his friends introduced him to the world of data science. "I attended a lecture where the presenter talked about Data Science as the 4th paradigm of scientific discovery.

We all in the hype of the AI world, but the reality is we are creating a world of Math and Science. It is as Simple as that. Yes, From Emotion to War field everywhere we apply math and check for feasibility for which we derive facts called Science. Now, in that prospect of understanding how this science is applied to Machines is the very raw side of Human life - a data life. For a better idea of getting it observed, here we have few books to take your life on Machine Learning.

Requirements No Such Pre-req, its good to have some basic math concepts Description Data Science is a multidisciplinary field that deals with the study of data. Data scientists have the ability to take data, understand it, process it, and extract information from it, visualize the information and communicate it. Data scientists are well-versed in multiple disciplines including mathematics, statistics, economics, business, and computer science, as well as the unique ability to ask interesting and challenging data questions based on formal or informal theory to spawn valuable and meticulous insights. This course introduces students to this rapidly growing field and equips them with its most fundamental principles, tools, and mindset. Have an in-depth understanding of the concepts of Machine Learning Be able to grasp, understand, and write machine learning code from scratch Use Builtin Libraries available to build machine learning models Be able to analyze, build, and assess models on any dataset Be able to interpret and understand the black box behind model Understand the applications of data science by exhibiting the ability to work on different datasets and interpreting them.

For almost all machine learning projects, the main steps of the ideal solution remains same. For each step, I was doing some research on the web depending on my business object and jotting down the best resources I ran across. The resources include Online Courses, Kernels from Kaggle, Cheat Sheets and Blog Posts. Below I've listed them and categorised by each step (all of the resources are free except the ones that have'paid' in the end):

Which machine learning algorithm should you use? It is a central question in applied machine learning. In a recent paper by Randal Olson and others, they attempt to answer it and give you a guide for algorithms and parameters to try on your problem first, before spot checking a broader suite of algorithms. In this post, you will discover a study and findings from evaluating many machine learning algorithms across a large number of machine learning datasets and the recommendations made from this study. Start With Gradient Boosting, but Always Spot Check Algorithms and Configurations Photo by Ritesh Man Tamrakar, some rights reserved.

Natural language processing tasks, such as caption generation and machine translation, involve generating sequences of words. Models developed for these problems often operate by generating probability distributions across the vocabulary of output words and it is up to decoding algorithms to sample the probability distributions to generate the most likely sequences of words. In this tutorial, you will discover the greedy search and beam search decoding algorithms that can be used on text generation problems. How to Implement Beam Search Decoder for Natural Language Processing Photo by See1,Do1,Teach1, some rights reserved. In natural language processing tasks such as caption generation, text summarization, and machine translation, the prediction required is a sequence of words.

Word embeddings provide a dense representation of words and their relative meanings. They are an improvement over sparse representations used in simpler bag of word model representations. Word embeddings can be learned from text data and reused among projects. They can also be learned as part of fitting a neural network on text data. In this tutorial, you will discover how to use word embeddings for deep learning in Python with Keras.