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<h2>About Courses</h2>
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<p align=justify>Organizations today operate in a world surrounded by data and data that is understood and analyzed smartly can play a pivotal role in determining the success of many businesses. Data Science, through its various inherent tools and techniques, is successfully adding value to all the business models by using statistics and deep learning to make better, relevant and timely decisions. Understanding the dynamics of data and knowing how to deal with data has therefore become a critical skill in an organizational context. No wonder that Harvard Business Review has termed Data Scientist as the Sexiest Job of the 21st Century!</p>
<p align=justify>The objective of this course is to introduce participants to the intricacies of data science and techniques of machine learning. This course will expose participants to hands on experience of popular and in-demand tools in the BDA and ML area and has been designed with an intention to impart practical problem solving skills to participants which in turn will enhance prospects of career growth in this sunrise domain.</p>
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<h3><b>Course-1</b></h3><p style="background-color:MediumSeaGreen;"><b>PG Certificate Program in Machine Learning and Big Data Analytics from CSTCP-IIIT Allahabad, Prayagraj</b></p>
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<h5><b>MODULE 1 – FUNDAMENTALS OF PYTHON</b></h5>
<p align=justify>Data scientists must know how to code – start by learning the fundamentals of one of the most popular programming languages – Python.</p>
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<ol>
<li>Basics of Python</li>
<li>Conditional and Loops</li>
<li>String and List Objects</li>
<li>Functions & OOPs Concepts</li>
<li>Exception Handling</li>
<li>Database Programming</li>
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<h5><b>MODULE 2 – DATA WRANGLING</b></h5>
<p align=justify>Once you have the core skill of programming covered– dip your feet in the nitty – gritties of working with data by learning how to wrangle and visualize them.</p>
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<li>Reading CSV, JSON, XML and HTML files using Python</li>
<li>NumPy & Pandas</li>
<li>Relational Databases and Data Manipulation with SQL</li>
<li>Scipy Libraries</li>
<li>Loading, Cleaning, Transforming, Merging, and Reshaping Data</li>
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<h5><b>MODULE 3 – STATISTICS AND PROBABILITY</b></h5>
<p align=justify>It is impossible to use data without knowledge of statistics. Collect, organize, analyze, interpret, and present data using these concepts of statistics.</p>
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<li>Descriptive Statistics & Data Distributions</li>
<li>Probability Concepts and Set Theory</li>
<li>Probability Mass Functions</li>
<li>Probability Distribution Functions</li>
<li>Cumulative Distribution Functions</li>
<li>Modeling Distributions</li>
<li>Inferential Statistics</li>
<li>Estimation</li>
<li>Hypothesis Testing</li>
<li>Implementation of Statistical Concepts in Python</li>
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<h5><b>MODULE 4 – MACHINE LEARNING MODELS IN PYTHON</b></h5>
<p align=justify>Machines have increased the ability to interpret large volumes of complex data. Combine aspects of computer science with statistics to formulate algorithms that help machines draw insights from structured and unstructured data.</p>
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<ol>
<li>Building Models Using Below Algorithms</li>
<li>Linear and Logistics Regression</li>
<li>Decision Trees</li>
<li>Support Vector Machines (SVMs)</li>
<li>Random Forests</li>
<li>XGBoost</li>
<li>K Nearest Neighbour & Hierarchical Clustering</li>
<li>Principal Component Analysis</li>
<li>Text Analytics and Time Series Forecasting</li>
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<h5><b>MODULE 5 – DATA VISUALIZATION USING MATPLOTLIB</b></h5>
<p align=justify>Complex data sets call for simple representations that are easy to follow. Visualize and communicate key insights derived from data effectively by using tools like Matplotlib.</p>
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<li>Interactive Visualizations with Matplotlib</li>
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<h5><b>MODULE 6 – DEEP LEARNING USING TENSORFLOW</b></h5>
<p align=justify>Go beyond superficial analysis of data by learning how to interpret them deeply. Use deep-learning nets to uncover hidden structures in even unlabeled and unstructured data using TensorFlow.</p>
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<li>Basics of Neural Network</li>
<li>Linear Algebra</li>
<li>Implementation of Neural Network in Vanilla</li>
<li>Basics of TensorFlow</li>
<li>Convolutional Neural Networks (CNNs)</li>
<li>Recurrent Neural Networks (RNNs)</li>
<li>Generative Models</li>
<li>Semi-supervised Learning using GAN</li>
<li>Seq-to-seq Model</li>
<li>Encoder and Decode</li
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</div> <p style="background-color:MediumSeaGreen;">End of Course-1</p>
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