This Applied Labs will guide the aspiring Data Scientist through hand on training starting from python foundation until machine learning. You will do a Hack day where you will run two machine learning algorithm for predictive modeling.
As Data Scientist, yours major time in beginning of experiment is learning about data, cleaning data, Data Scientist in the beginning is called janitorial data cleaning. You will learn how to load data from various popular sources like excel, web scrapping data, loading data from any database. We have a complete session devoted towards during web scrapping from ground up. We will teach you how to write your own web scraping code. We also teach you few noncoding technique like Kimono Labs.
You will also learn Data visualization techniques into Python. Data visualization is used to analyzes your data and represent the results of your experiment. You will masters basic line plot, histogram, scatter plot, box plot, we also give you in detail how to label you plots how to create canvas etc. We also help you to learn some comparison of using metplotlib, pandas and D3.JS and give you inside of power pi and azure machine learning and AWS Machine learning.
We specialize in our machine learning. We will give you a good understanding of statistics the model like linear regression, logistics regression, classification models, K mean the nearest point and Random forest. You will be specializing in understanding and coding these algorithms with the instructors.
By the end of this course, you will be able to apply predictive modeling, you will take two projects on Hack Day using machine learning. We also help you to build your own recommendation model. You will be assigned a mentor post applied labs for any further question on these topics. You will also be included in data science community which we have built called Einstein Assembly. Where you can part of our science community and work on few projects.
From working on the hack day project, you will be exposed to and understand the skills that are needed to become a data scientist. You will also be armed for the 8 Essential Skills Kicking Data Scientist Career
In this workshop you’ll learn an in-depth process of Data Science:
This is a very practical and hands-on workshop that has lots of class exercises. Through this course, we strive to make you fully equipped to become a developer who can execute full-fledged Data Science projects.
You must bring a laptop with a text editor.
Sublime Text is recommended and has a free trial version (http://www.sublimetext.com/).
In addition, students should install Anaconda, which is a free package that includes python and a number of tools that will be used in class (http://continuum.io/downloads).
Anyone taking this course should have some minimum experience with any programming with preferred some basic understanding about Python, or any other programming language. If not…… We offer our Python Foundation Course for free!!!
In our first class, we will go over some Python fundamentals, which will cover syntax and built-in functions. We will move onto practicing For Loops and introducing the packages that will be covered over the course and how to install them.
We will start by introducing NumPy and Pandas and showcasing how to clean, manipulate, and analyze data. Students will practice on the Titanic dataset before moving onto web scraping techniques and extracting data from APIs.
We will begin by reviewing NumPy and Pandas before delving deeper into more advanced techniques to clean and munge data. Using Matplotlib and Seaborn packages, students will learn to visualize data and identify trends.
We will be introducing the Cross Industry Standard Process for Data Mining (CRISP-DM) and data mining with supervised learning and unsupervised learning. Afterwards, students will explore machine learning algorithms such as Regression (Linear, Multivariable, and Logistic), Naïve Bayes, Decision Trees, and Clustering.
Students will review machine learning concepts and will start by building their own recommendation system with a MovieLens dataset, understanding dimension reduction with Principal Component Analysis, exploring Support Vector Machines, and learning A/B Testing with T-Tests and P-Values.
Students will explore the Natural Language Toolkit to process and extract text data. Students will then start a Natural Language Processing project with Yelp data before we move onto Sentimental Analysis to predict positive versus negative Yelp reviews.
Students will be introduced to Big Data and data engineering with the Hadoop ecosystem, the MapReduce paradigm, and the up-and-coming Apache Spark.
We will be introducing deep learning and training neural network and visualizing what a neural network has learned using TensorFlow Playground. Students will also learn time series, what makes them special, loading and handling time series in Pandas. Understand how seasonality affects trends.
Students will be introduced to computer vision fundamentals using OpenCV to detect faces, people, cars, and other objects. We will conclude the day with a hack challenge. Students will be grouped into teams and will showcase their group project at the end of class.
In the last session, we will host a private Kaggle competition amongst the students. Students will be grouped into teams and will showcase their group project at the end of class.
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