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Udemy - Data Science using Machine Learning Algorithm with Big Data

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Added 5 years ago by tutsnode in Other
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Udemy - Data Science using Machine Learning Algorithm with Big Data (Size: 5.2 GB)
  0 204.8 B
  1. Collection of Data and Tools to Collect Data.mp4 108.1 MB
  1. Data Processing, Analytic and Manipulation with Pandas.mp4 14.3 MB
  1. Data Processing, Analytic and Manipulation with Pandas.srt 2.9 KB
  1. How to Learn Mathematics for Machine Learning.mp4 40.8 MB
  1. Importance of Data Visualization.mp4 29.9 MB
  1. Importance of Data Visualization.srt 5.8 KB
  1. Introduction of ML.srt 0 B
  1. Introduction of Python.mp4 58.9 MB
  1. Introduction of Python.srt 9.9 KB
  1. Linear Regression.mp4 50.9 MB
  1. Linear Regression.srt 7.9 KB
  1. Statistical Data Analysis.mp4 39.4 MB
  1. Statistical Data Analysis.srt 6.2 KB
  1. Why to join this course.mp4 21.5 MB
  1. Why to join this course.srt 1.6 KB
  1 1.1 KB
  1. Case Study 1 with Scikit Learn Library.mp4 278.1 MB
  1. Case Study 1 with Scikit Learn Library.srt 42.8 KB
  1. Collection of Data and Tools to Collect Data.srt 19.1 KB
  1. How to Learn Mathematics for Machine Learning.srt 7.9 KB
  1. Introduction of ML.mp4 1.3 MB
  1. Operations Possible on Data with Numpy.mp4 8.2 MB
  1. Operations Possible on Data with Numpy.srt 2.1 KB
  1.1 Material.zip 1.7 KB
  2 102.4 B
  10. Seaborn Library Tutorial 3.mp4 42 MB
  10. Seaborn Library Tutorial 3.srt 5 KB
  10. Tuple Operations in details.mp4 42.9 MB
  10. Tuple Operations in details.srt 6.7 KB
  11. Plotly Library Tutorial.mp4 318.7 MB
  11. Plotly Library Tutorial.srt 39.9 KB
  11. String Operation in Python.mp4 53.3 MB
  11. String Operation in Python.srt 9.5 KB
  11.1 Plotly.zip 2.4 KB
  12. Types of Operators.mp4 51.2 MB
  12. Types of Operators.srt 9.5 KB
  13. Generation of Random Number and Range Functions.mp4 53 MB
  13. Generation of Random Number and Range Functions.srt 8.3 KB
  14. Data Type Conversion.mp4 68.3 MB
  14. Data Type Conversion.srt 11.5 KB
  15. Math library.mp4 25.9 MB
  15. Math library.srt 5 KB
  16. Importance of Indentation.mp4 38 MB
  16. Importance of Indentation.srt 6.5 KB
  17. Sequential, Selection, Repetition.mp4 52.4 MB
  17. Sequential, Selection, Repetition.srt 13.6 KB
  18. User Define Functions and inbuilt Function.mp4 41.3 MB
  18. User Define Functions and inbuilt Function.srt 8.1 KB
  19. Python CSV file Operations.mp4 61.4 MB
  19. Python CSV file Operations.srt 7.8 KB
  2. Case Study 2 with Scikit Learn Library.mp4 411.8 MB
  2. Case Study 2 with Scikit Learn Library.srt 60.6 KB
  2. Environment Set up.mp4 40.6 MB
  2. Environment Set up.srt 8.4 KB
  2. How to choose the RIGHT Charts & Graph for your Data.mp4 87 MB
  2. How to choose the RIGHT Charts & Graph for your Data.srt 8.8 KB
  2. Importance of Data Analysis and Data Science.mp4 55.6 MB
  2. Importance of Data Analysis and Data Science.srt 8.2 KB
  2. Logistic Regression.mp4 88.2 MB
  2. Logistic Regression.srt 14.4 KB
  2. Main Challenges of Machine Learning.mp4 45.2 MB
  2. Main Challenges of Machine Learning.srt 9.9 KB
  2. Mega Mart Example for Increase Sale.mp4 74.8 MB
  2. Mega Mart Example for Increase Sale.srt 15.9 KB
  2. Numpy Library Tutorial 1.mp4 218.1 MB
  2. Numpy Library Tutorial 1.srt 40.1 KB
  2. Pandas Tutorial 1.mp4 189.5 MB
  2. Pandas Tutorial 1.srt 30.3 KB
  2. What is Machine Learning.mp4 126.8 MB
  2. What is Machine Learning.srt 10.3 KB
  2.1 Material.zip 23.7 KB
  2.1 Support file for Practice.zip 214.1 KB
  20. Python Crash Course.mp4 254.4 MB
  20. Python Crash Course.srt 45.2 KB
  20.1 Programs.zip 4.1 KB
  3. Data Type, Variable and Keywords.mp4 64.6 MB
  3. Data Type, Variable and Keywords.srt 12.1 KB
  3. Introduction of Machine Learning.mp4 60.4 MB
  3. Matplotlib Library Tutorial 1.mp4 204.2 MB
  3. Matplotlib Library Tutorial 1.srt 30.3 KB
  3 0 B
  3. Introduction of Machine Learning.srt 14.8 KB
  3. Numpy Library Tutorial 2.mp4 79.1 MB
  3. Numpy Library Tutorial 2.srt 13 KB
  3. Pandas Tutorial 2.mp4 142.6 MB
  3. Pandas Tutorial 2.srt 19.5 KB
  3. Reasons to Learn Probability for Machine Learning.mp4 42 MB
  3. Reasons to Learn Probability for Machine Learning.srt 5.1 KB
  3. Supervise Machine Learning.mp4 25.5 MB
  3. Supervise Machine Learning.srt 3.2 KB
  3. Support Vector Machines (SVM).mp4 71.4 MB
  3. Support Vector Machines (SVM).srt 14.9 KB
  3. Why Mega Mart Gives Discount.mp4 36.6 MB
  3. Why Mega Mart Gives Discount.srt 8.3 KB
  3.1 .ipynb files.zip 261.4 KB
  3.1 Material.zip 1.4 KB
  3.1 ipynp files.zip 179.2 KB
  4. Confusion Matrix with Covid 19 Patients Data.mp4 36.2 MB
  4. Confusion Matrix with Covid 19 Patients Data.srt 13.2 KB
  4. Dimention Reduction is Curse in Machin Learning.mp4 63.1 MB
  4. Dimention Reduction is Curse in Machin Learning.srt 7.6 KB
  4. How to produce output Print Statement in Python.mp4 28.8 MB
  4. How to produce output Print Statement in Python.srt 4.2 KB
  4. Introduction of Big Data.mp4 56.4 MB
  4. Introduction of Big Data.srt 11.4 KB
  4. K Mean Algorithm.mp4 49.9 MB
  4. K Mean Algorithm.srt 8.1 KB
  4. Matplotlib Library Tutorial 2.mp4 55.9 MB
  4. Matplotlib Library Tutorial 2.srt 8.8 KB
  4. Numpy Library Tutorial 3.mp4 108.9 MB
  4 849.5 KB
  4. Numpy Library Tutorial 3.srt 17 KB
  4. Pandas Tutorial 3.mp4 91.7 MB
  4. Pandas Tutorial 3.srt 13.9 KB
  4. Training, Testing and Model Evaluation in Machine Learning.mp4 29.9 MB
  4. Training, Testing and Model Evaluation in Machine Learning.srt 8.2 KB
  4.1 Material.zip 1 MB
  5 805.1 KB
  5. How to take input .mp4 19.6 MB
  5. How to take input .srt 3.9 KB
  5. KNN Algorithm.mp4 86.9 MB
  5. KNN Algorithm.srt 12 KB
  5. Matplotlib Library Tutorial 3.mp4 69.8 MB
  5. Matplotlib Library Tutorial 3.srt 9.6 KB
  5. Numpy Library Tutorial 4.mp4 72.3 MB
  5. Numpy Library Tutorial 4.srt 9.4 KB
  5. Pandas Tutorial 4.mp4 152.6 MB
  5. Pandas Tutorial 4.srt 23.3 KB
  5.1 Material.zip 436.3 KB
  6 483.8 KB
  6. List, Tuple, Set, Dictionary.mp4 18.5 MB
  6. List, Tuple, Set, Dictionary.srt 3.9 KB
  6. Matplotlib Library Tutorial 4.mp4 42.7 MB
  6. Matplotlib Library Tutorial 4.srt 6.1 KB
  6. Numpy Library Tutorial 5.mp4 43.5 MB
  6. Numpy Library Tutorial 5.srt 5.6 KB
  7 395.1 KB
  7. List Operations in details.mp4 63.2 MB
  7. List Operations in details.srt 10.5 KB
  7. Matplotlib Library Tutorial 5.mp4 36.2 MB
  7. Matplotlib Library Tutorial 5.srt 3.9 KB
  7. Numpy Library Tutorial 6.mp4 24.6 MB
  7. Numpy Library Tutorial 6.srt 3.3 KB
  8. Numpy Library Tutorial 7.mp4 19.1 MB
  8 401.9 KB
  8. Numpy Library Tutorial 7.srt 4.1 KB
  8. Seaborn Library Tutorial 1.mp4 82.2 MB
  8. Seaborn Library Tutorial 1.srt 14.4 KB
  8. Set Operations in details.mp4 26.1 MB
  8. Set Operations in details.srt 4.4 KB
  8.1 .ipynb files.zip 2.3 KB
  9. Dictionary Operations in details.mp4 28.5 MB
  9. Dictionary Operations in details.srt 4.5 KB
  9. Numpy Official Site Visit.mp4 25.8 MB
  9. Numpy Official Site Visit.srt 2.9 KB
  9. Seaborn Library Tutorial 2.mp4 113.8 MB
  9. Seaborn Library Tutorial 2.srt 15.6 KB
  TutsNode.com.txt 102.4 B
  [TGx]Downloaded from torrentgalaxy.to .txt 614.4 B
  9 191.9 KB
  10 212.5 KB
  11 143 KB
  12 970.3 KB
  13 259.4 KB
  14 782.3 KB
  16 118.8 KB
  17 864.5 KB
  18 962 KB
  19 225.5 KB
  20 713.4 KB
  21 592.3 KB
  22 216.6 KB
  23 710.2 KB
  24 407.8 KB
  25 833 KB
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  28 587.5 KB
  29 83.3 KB
  30 647.7 KB
  31 83.2 KB
  32 427 KB
  33 698.9 KB
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  36 842.2 KB
  37 59.7 KB
  38 132.7 KB
  39 814.9 KB
  40 486.2 KB
  41 140.8 KB
  42 357.5 KB
  43 996.8 KB
  45 715.4 KB
  46 247.9 KB
  47 447.4 KB
  48 650.9 KB
  49 34.1 KB
  50 449.6 KB
  51 782.4 KB
  52 867.2 KB
  53 70.4 KB
  54 89.5 KB
  55 166 KB
  56 509.4 KB
  57 944 KB
  58 103.1 KB
  59 174 KB
  60 519.9 KB
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  63 387.4 KB
  64 965.3 KB
  65 545.4 KB
  66 729.3 KB
  67 830.6 KB
  68 704.8 KB
  ▲ 224 total files

Description


Description

This Course will design to understand Data Science using Machine Learning Algorithms with big data concept. Big data Analysis covered with machine learning algorithms. This Course divide in three part. Part 1 focus on Data Science with all important concept, Part 2 focus on Machine Learning with all necessary algorithms, Part 3 focus on Big Data with basic fundamental. The Machine Learning Algorithms such as Linear Regression, Logistic Regression, SVM, K Mean, KNN, Naïve Bayes, Decision Tree and Random Forest are covered with case studies. The course provides path to start career in Data Science, Machine Learning and big data . Machine Learning Types such as Supervise Learning, Unsupervised Learning, Reinforcement Learning are also covered. Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered.

Machine Learning- Machine learning is the field of study that focuses on computer systems that can learn from data. That is the system’s often called models can learn to perform a specific task by analyzing lots of examples for a particular problem. For example, a machine learning model can learn to recognize an image of a dog by being shown lots and lots of images of dogs.

What is Data Science- Data science is an inter-disciplinary field of Computer Science that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data.

Big Data- it is a collection of data that is huge in volume, yet growing exponentially with time. It is a data with so large size and complexity that none of traditional data management tools can store it or process it efficiently. Big data is also a data but with huge size.
Who this course is for:

The course is ideal for all, as it starts from the fundamentals and gradually builds up your skills in Data Science ,Machine Learning and Big Data concept

Requirements

It start with Basics
All software used in this course is either available for Free or as a Demo version
This course is intended for absolute beginners in programming

Last Updated 1/2021

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