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Python for Data Science | Python Data Science Tutorial | Data Science Certification | Edureka

( Python Data Science Training : https://www.edureka.co/python ) <br>This Edureka video on "Python For Data Science" explains the fundamental concepts of data science using python. It will also help you to analyze, manipulate and implement machine learning using various python libraries such as NumPy, Pandas and Scikit-learn. <br><br>This video helps you to learn the below topics: <br>1. Need of Data Science <br>2. What is Data Science? <br>3. How Python is used for Data Science? <br>4. Data Manipulation in Python <br>5. Implement Machine Learning using Python <br>6. Demo <br><br>Subscribe to our channel to get video updates. Hit the subscribe button above. <br><br>Check out our Python Training Playlist: https://goo.gl/Na1p9G

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Python for Data Science | Python Data Science Tutorial | Data Science Certification | Edureka

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  1. Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  2. Agenda 01 Need of Data Science 02 What Is Data Science? 03 How Python Is Used For Data Science? 04 Data Manipulation In Python 05 Implement Machine Learning Using Python 06 Demo Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  3. Need Of Data Science Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  4. Need Of Data Science ❖ To handle and analyze extremely THEN THEN large datasets/ data flow ❖ Faster & better Decision making ❖ No Predictions ❖ Reduce Production Cost ❖ Gain business Insights NOW ❖ Build intelligence & ability in machines Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  5. What Is Data Science? Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  6. What Is Data Science? Data Science, known as data driven science makes use of scientific methods, processes, algorithms and systems to extract knowledge or insights with the goal to discover hidden patterns from the raw data. Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  7. Data Life Cycle Data Scientist provides a ONE STOP SOLUTION for all these operations Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  8. Programming Languages For Data Science Python Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  9. Programming Languages For Data Science Python Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  10. Python For Data Science Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  11. Python For Data Science 01 It is simple and easy to learn Python Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  12. Python For Data Science 01 It is simple and easy to learn 02 Python Fit for many platforms Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  13. Python For Data Science 01 It is simple and easy to learn 02 Python Fit for many platforms 03 03 It is high level and interpreted language Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  14. Python For Data Science 01 It is simple and easy to learn Perform data manipulation, analysis and visualization 02 Python 04 04 Fit for many platforms 03 03 It is high level and interpreted language Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  15. Python For Data Science Powerful libraries for Machine learning applications & other scientific computations 01 It is simple and easy to learn 05 Perform data manipulation, analysis and visualization 02 Python 04 04 Fit for many platforms 03 03 It is high level and interpreted language Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  16. Data Manipulation Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  17. Data Manipulation Using data manipulation, you can extract, filter and transform your data quickly and efficiently. LIBRARIES USED: NumPy Pandas Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  18. NumPy & Pandas NumPy Pandas NumPy is a Python package which stands for ‘Numerical Python Pandas is built on top of NumPy. It is used for data manipulation and analysis. conda install numpy conda install pandas import numpy import pandas Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  19. Demo: Basic Operations Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  20. Overview of Machine Learning Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  21. Machine Learning Machine learning is a type of Artificial Intelligence that allows software applications to learn from the data and become more accurate in predicting outcomes without human intervention. Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  22. Types Of Machine Learning 1 Supervised Learning 2 Unsupervised Learning 3 Reinforcement Learning Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  23. Types Of Machine Learning 1 Supervised Learning 2 Unsupervised Learning 3 Reinforcement Learning Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  24. Supervised Learning Supervised Learning is where you have input variable (X) and output variable (Y) and you use an algorithm to learn the mapping function from the input to the output. Y = f(X) ALGORITHMS: Logistic Regression Decision Tree Linear Regression Naïve Bayes Classifier Random forest Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  25. Demo: Logistic Regression Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  26. Logistic Regression ❑ Logistic regression produces results in a binary format ❑ Used to predict outcome of a categorical dependent variable ❑ Outputs – Yes/ no, true/ false, high/ low, pass/ fail Relation Between Dependent & Independent variable: Unknown value of variable or the variable to be predicted known variable or the variable related to dependent variable Dependent Variable Independent Variable Y = a + bX Y It is the tangent angle made by the line It is a point at which the line cuts the y- axis Slope intercept Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  27. Demo: Logistic Regression PROBLEM STATEMENT A car company has released a new SUV in the market. Using the previous data about the sales of their SUV’s, they want to predict the category of people who might be interested in buying this. Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  28. Types Of Machine Learning 1 Supervised Learning 2 Unsupervised Learning 3 Reinforcement Learning Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  29. Unsupervised Learning Unsupervised Learning is the training of a model using information that is neither classified or labelled. Unsupervised learning is also called as clustering analysis. ALGORITHMS: Hierarchical Clustering K- Means Clustering Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  30. Types Of Machine Learning 1 Supervised Learning 2 Unsupervised Learning 3 Reinforcement Learning Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  31. Reinforcement Learning It is an area of machine learning where a RL agent learns from the consequences of its actions, rather than from being taught explicitly. It selects its actions on basis of its past experiences (exploitation) and also by new choices (exploration). ALGORITHMS: Q- learning SARSA DQN Copyright © 2018, edureka and/or its affiliates. All rights reserved.

  32. Session In A Minute Need Of Data Science What is Data Science? Python For Data Science Demo Implement ML Data Manipulation Supervised Learning Unsupervised Learning Reinforcement Learning

  33. Copyright © 2017, edureka and/or its affiliates. All rights reserved.

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