Who This Book Is For This title is for data scientist and researchers who are already into the field of data science and want to see machine learning in action and explore its real-world application. Prior knowledge of Python programming and mathematics is must with basic knowledge of machine learning concepts. It is one of the fastest growing trends in modern computing, and everyone wants to get into the field of machine learning. In order to obtain sufficient recognition in this field, one must be able to understand and design a machine learning system that serves the needs of a project.
The idea is to prepare a learning path that will help you to tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques. Also, it will give you a solid foundation in the machine learning design process, and enable you to build customized machine learning models to solve unique problems. The course begins with getting your Python fundamentals nailed down. It focuses on answering the right questions that cove a wide range of powerful Python libraries, including scikit-learn Theano and Keras. After getting familiar with Python core concepts, it's time to dive into the field of data science.
You will further gain a solid foundation on the machine learning design and also learn to customize models for solving problems. At a later stage, you will get a grip on more advanced techniques and acquire a broad set of powerful skills in the area of feature selection and feature engineering. Style and approach This course includes all the resources that will help you jump into the data science field with Python. The aim is to walk through the elements of Python covering powerful machine learning libraries.
This course will explain important machine learning models in a step-by-step manner. Each topic is well explained with real-world applications with detailed guidance.
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Through this comprehensive guide, you will be able to explore machine learning techniques. Product details Format Paperback pages Dimensions x x He has been ranked as the number one most inflential data scientist on GitHub by Analytics Vidhya.
DEEPER INSIGHTS (INSIGHTS Series Book 3)
He has many years of experience with coding in Python and he has conducted several seminars on the practical applications of data science and machine learning. Talking and writing about data science, machine learning, and Python really motivated Sebastian to write this book in order to help people develop data-driven solutions without necessarily needing to have a machine learning background. He has also actively contributed to open source projects and methods that he implemented, which are now successfully used in machine learning competitions, such as Kaggle.
In his free time, he works on models for sports predictions, and if he is not in front of the computer, he enjoys playing sports. I would like to thank my professors, Arun Ross and Pang-Ning Tan, and many others who inspired me and kindled my great interest in pattern classifiation, machine learning, and data mining. I would like to take this opportunity to thank the great Python community and developers of open source packages who helped me create the perfect environment for scientifi research and data science.
A special thanks goes to the core developers of scikit-learn. As a contributor to this project, I had the pleasure to work with great people, who are not only very knowledgeable when it comes to machine learning, but are also excellent programmers. He is currently collecting a labeled training set that includes images and environmental data temperature, humidity, soil moisture, and pH , linking this data to observations of infestation the target variable , and using it to train neural net models.
The aim is to create a model that will reduce the need for direct observation, be able to anticipate insect outbreaks, and subsequently control conditions.
David also works as a data analyst, I. Science shows that people love to talk about themselves — even as much as they love money. It also makes them feel important to know that a brand cares about their feedback, which feeds their egos. Shoot for about 5 people per customer category or persona — but no more than a dozen. The last thing you want is to come across as a robotic telemarketer, asking one carefully crafted question after another. Your No.http://danardono.com.or.id/libraries/2020-08-18/butyf-the-best-phone.php
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This holds true for customer interviews as well. Your questions should be fluid, always changing based on the responses you get from the customer to glean the most insight. But having said that, you still need a starting point.
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Exactly what you ask should be driven by your main goals for the interview. You can find some great sample interview questions here and here. Here are some topics you may want to cover:.
But the most insightful responses — especially for copywriting and messaging — will often come from asking your customer how they feel. Yes, these questions do sound a bit corny. And yes, the interviewees might chuckle a little when you ask them.
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But they will help you get to the deeper emotional drivers behind why and how your customers buy. Bias is always an issue in qualitative research. But the more aware you are of the pitfalls, the easier it will be get meaningful data. For example:. Asking someone why they did something implies that there is a single right answer. Not a rationalized answer. But having said that, asking why quickly and in an unexpected way can sometimes bring out a more emotional response. And over time, your own interview style will develop.
So what follows is more of a rough guide for handling the chat. I ultimately want to emphasize the importance of three things:. Image Source. Research shows that creating rapport with your interviewee is key to getting them to be comfortable enough with you to share their feelings. You can also use Charlie app to get a snapshot of their social data automatically. Doing this eases the customer into the interview.
Afterwards, you can then watch for opportunities to build rapport. I often start with asking about where they live or what they do for a living.
Not only does this give you more demographic or even psychographic information, it also provides you with fodder for directing the conversion into a more casual place before getting into the meatier questions. We love backpacking and rafting, but I always seem to end up working weekends. Make them feel like they are an expert and you are privileged to speak with them — which is really the truth. Be sure to use their name as much as possible — this helps to boost their ego and self-esteem , which will make them feel more confident opening up to you. But you have to do this in a very subtle way during an interview — match their energy level, the volume of their voice or the pace of their speech.
Plus, the issue of building too much rapport is a concern for some qualitative researchers. The customer could manufacture an answer to please you, rather than giving you the unvarnished truth. The art of genuine listening means that you really strive to understand what the other person is telling you, without projecting yourself into the conversation. A good reflective listener will occasionally paraphrase what the customer says during the interview, which shows they are listening and making efforts to understand what the person is telling them. Interviewer: So if I understand you correctly, you had a hard time choosing what to buy because Product X and Product Y seemed to have the same exact features.
Sure, it might sound easy enough now. Next time you ask a question, they may second-guess their response. This is a tricky one. An interview is about not just what is said, but how the person is saying it. This includes altering how fast they speak or suddenly raising their voice. These subtle changes can sometimes be a cue on when to move onto another topic, request clarification or ask deeper questions.
Noticing paralanguage takes time to develop. Probing questions are essential to get at the deeper meaning behind what the person says. Probing is especially important when asking questions about how they feel. This allows you get to the deeper barriers, problems or motivators for your audience.
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For example, you might ask:. There are also more subtle ways to probe for deeper insights. You might want to give these techniques a try:. If something the customer says stands out as unusually insightful, try to get a more specific description from them about the event. I hate wasting time trying to find what I want. People are naturally inclined to fill long, awkward moments of silence with conversation. A well-timed stretch of silence, especially after an important question that gets a short response, can result in the interviewee feeling compelled to yak on.
This is a simple technique that also touches on reflective listening. Simply repeat the last thing that the person said and encourage them to continue. It works particularly well when the interviewee is talking about a specific event or telling a story.