Udemy - MACHINE LEARNING MASTER CLASS, AI MADE EASY (Zero to Hero


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Torrent Hash : 89A416054201781C60DF1B3747D9F7E42DD48357
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Torrent File Content (586 files)


MACHINE LEARNING MASTER CLASS, AI MADE EASY (Zero to Hero!!)
    [TutsNode.com] - 20. Multiple linear regression
          [TutsNode.com] - 20. Multiple linear regression/2. Multiple linear regression behind the scene - Part 1.mp4 -
160.26 MB

     TutsNode.com.txt -
63 bytes

    [TutsNode.com] - 21. Polynomial regression
          [TutsNode.com] - 21. Polynomial regression/2. Polynomial regression on multiple feature dataset.srt -
27.97 KB

    [TutsNode.com] - 34. Naive bayes classification
          [TutsNode.com] - 34. Naive bayes classification/4. The log scale.srt -
26.21 KB

    [TutsNode.com] - 16. Visualisation ( Exploratory Data Analysis) with Seaborn
          [TutsNode.com] - 16. Visualisation ( Exploratory Data Analysis) with Seaborn/5. Seaborn plots.srt -
26.15 KB

    [TutsNode.com] - 3. Python Statements
          [TutsNode.com] - 3. Python Statements/6. Range, enumerate and zip.srt -
25.78 KB

    [TutsNode.com] - 12. Python Pandas
          [TutsNode.com] - 12. Python Pandas/2. DataFrame introduction.srt -
25.46 KB

    [TutsNode.com] - 14. Python Matplotlib
          [TutsNode.com] - 14. Python Matplotlib/3. Matplotlib Subplot and histogram.srt -
25.37 KB

    [TutsNode.com] - 30. ML Concept - K-Fold validation, GridSearch
          [TutsNode.com] - 30. ML Concept - K-Fold validation, GridSearch/2. Updated template with GridSearchCV.srt -
24.17 KB

    [TutsNode.com] - 16. Visualisation ( Exploratory Data Analysis) with Seaborn
          [TutsNode.com] - 16. Visualisation ( Exploratory Data Analysis) with Seaborn/2. Scatter plot on Iris dataset.srt -
23.26 KB

    [TutsNode.com] - 21. Polynomial regression
          [TutsNode.com] - 21. Polynomial regression/1. Polynomial regression.srt -
23 KB

    [TutsNode.com] - 1. Python Setting up
          [TutsNode.com] - 1. Python Setting up/5. Meet your Author.srt -
2.47 KB

          [TutsNode.com] - 1. Python Setting up/6. Linkedin and Instagram links.html -
511 bytes

    [TutsNode.com] - 29. Regression - Regression models master template
          [TutsNode.com] - 29. Regression - Regression models master template/1. Master template regression model - Data creation.srt -
22.85 KB

    [TutsNode.com] - 34. Naive bayes classification
          [TutsNode.com] - 34. Naive bayes classification/1. Bayes theorem.srt -
22.2 KB

    [TutsNode.com] - 19. Linear Regression
          [TutsNode.com] - 19. Linear Regression/2. Linear regression implementation in python - Part 1.srt -
21.97 KB

    [TutsNode.com] - 9. Python Regular expression
          [TutsNode.com] - 9. Python Regular expression/5. BeginsWith endsWith and dot character.srt -
21.9 KB

    [TutsNode.com] - 23. Decision Tree regression
          [TutsNode.com] - 23. Decision Tree regression/1. Measuring Entropy & Gini impurity.srt -
21.27 KB

    [TutsNode.com] - 42. Evaluation techniques using curves (ROC,AUC, PR, CAP)
          [TutsNode.com] - 42. Evaluation techniques using curves (ROC,AUC, PR, CAP)/3. ROC, AUC - Calculating the optimal threshold (Youdens method).srt -
21.02 KB

    [TutsNode.com] - 20. Multiple linear regression
          [TutsNode.com] - 20. Multiple linear regression/2. Multiple linear regression behind the scene - Part 1.srt -
20.98 KB

    [TutsNode.com] - 4. Python Method and Functions
          [TutsNode.com] - 4. Python Method and Functions/5. Maps, Filters and Lambdas.srt -
20.81 KB

    [TutsNode.com] - 34. Naive bayes classification
          [TutsNode.com] - 34. Naive bayes classification/5. Gaussian naive bayes.srt -
20.8 KB

    [TutsNode.com] - 18. Pre-processing
          [TutsNode.com] - 18. Pre-processing/4. Test and train data split and Feature scaling.srt -
20.78 KB

          [TutsNode.com] - 18. Pre-processing/7. Assignment solution and OneHotEncoding - Part 01.srt -
20.19 KB

    [TutsNode.com] - 42. Evaluation techniques using curves (ROC,AUC, PR, CAP)
          [TutsNode.com] - 42. Evaluation techniques using curves (ROC,AUC, PR, CAP)/7. CAP curve with multiple models and multi-class.srt -
20.08 KB

    [TutsNode.com] - 5. Python Module and packages
          [TutsNode.com] - 5. Python Module and packages/2. User defined packages.srt -
20.06 KB

    [TutsNode.com] - 6. Python OOPS in python
          [TutsNode.com] - 6. Python OOPS in python/4. Multiple, multi level inheritance and MRO.srt -
19.95 KB

    [TutsNode.com] - 22. Before we move forward
          [TutsNode.com] - 22. Before we move forward/2. Gradient decent - Background.srt -
19.68 KB

    [TutsNode.com] - 31. Pre-processing revisited
          [TutsNode.com] - 31. Pre-processing revisited/5. Pre-processing re-visited.srt -
19.53 KB

          [TutsNode.com] - 31. Pre-processing revisited/1. Why Co-relation is important.srt -
19.24 KB

    [TutsNode.com] - 11. Python Numpy
          [TutsNode.com] - 11. Python Numpy/5. Matrices selection and conditional selection.srt -
19.23 KB

    [TutsNode.com] - 18. Pre-processing
          [TutsNode.com] - 18. Pre-processing/8. Assignment solution and OneHotEncoding - Part 02.srt -
19.21 KB

    [TutsNode.com] - 30. ML Concept - K-Fold validation, GridSearch
          [TutsNode.com] - 30. ML Concept - K-Fold validation, GridSearch/3. K Fold cross validation without GridSearchCV.srt -
19.21 KB

    [TutsNode.com] - 10. Python Files
          [TutsNode.com] - 10. Python Files/3. Read mode, write mode and methods.srt -
19.09 KB

    [TutsNode.com] - 43. Ensemble techniques
          [TutsNode.com] - 43. Ensemble techniques/1. Voting classifier.srt -
19.05 KB

    [TutsNode.com] - 25. Bagging and boosting
          [TutsNode.com] - 25. Bagging and boosting/2. Boosting.srt -
18.83 KB

    [TutsNode.com] - 12. Python Pandas
          [TutsNode.com] - 12. Python Pandas/3. DataFrame Selections.srt -
18.7 KB

    [TutsNode.com] - 13. More useful modules
          [TutsNode.com] - 13. More useful modules/1. Python random class.srt -
18.64 KB

    [TutsNode.com] - 35. Few good things to know about ML
          [TutsNode.com] - 35. Few good things to know about ML/1. Euler's number.srt -
18.11 KB

    [TutsNode.com] - 32. Classification - K-nearest neighbors algorithm (KNN)
          [TutsNode.com] - 32. Classification - K-nearest neighbors algorithm (KNN)/1. KNN background.srt -
18.09 KB

    [TutsNode.com] - 8. Python decorators and Generators
          [TutsNode.com] - 8. Python decorators and Generators/1. Python decorators.srt -
17.72 KB

    [TutsNode.com] - 16. Visualisation ( Exploratory Data Analysis) with Seaborn
          [TutsNode.com] - 16. Visualisation ( Exploratory Data Analysis) with Seaborn/8. Boxplot and Violin Plot.srt -
17.65 KB

    [TutsNode.com] - 13. More useful modules
          [TutsNode.com] - 13. More useful modules/2. Random under numpy and Arange.srt -
17.6 KB

    [TutsNode.com] - 6. Python OOPS in python
          [TutsNode.com] - 6. Python OOPS in python/6. Special class methods.srt -
17.52 KB

    [TutsNode.com] - 31. Pre-processing revisited
          [TutsNode.com] - 31. Pre-processing revisited/2. Co-variance.srt -
17.5 KB

    [TutsNode.com] - 8. Python decorators and Generators
          [TutsNode.com] - 8. Python decorators and Generators/3. Python generators.srt -
17.5 KB

    [TutsNode.com] - 28. Regression - Evaluation technique background (Regression)
          [TutsNode.com] - 28. Regression - Evaluation technique background (Regression)/1. R-square.srt -
17.49 KB

    [TutsNode.com] - 20. Multiple linear regression
          [TutsNode.com] - 20. Multiple linear regression/3. Multiple linear regression behind the scene - Part 2.srt -
17.37 KB

    [TutsNode.com] - 36. Classification - Support Vector machines
          [TutsNode.com] - 36. Classification - Support Vector machines/1. SVM getting started with 1D data.srt -
17.25 KB

    [TutsNode.com] - 13. More useful modules
          [TutsNode.com] - 13. More useful modules/3. Python collections.srt -
17.05 KB

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Description




Description

Machine learning is the study of computer algorithms that improve automatically through experience, it’s one of the most well-liked and top paying skills. Machine learning is also one of the most interesting fields to work on. They are typically used to solve various types of life problems. In the older days, Machine Learning tasks were time-consuming, tedious, and inefficient. In the modern days, it’s become considerably easy and efficient compared to the olden days by various Python libraries, frameworks, and modules.

What makes ML so popular to learn?

In this technology-reliant world, we are gradually traversing towards new and better processing techniques. As a result, to sustain the hunger for technology machine learning is considered as its answer. ML is one of the technologies which is expected to revolutionize the future. From trivial jobs to sophisticated services, everything is using ML today. Machine learning is not just learning but it’s also about understanding and reasoning. It is one of the widely used techniques to apply complex algorithms on a particular dataset to create a model for a data science project.

If you’re curious about the sector of Machine Learning? Then this course is for you!

In this course, we’ll cover all concepts, functions, and required topics which will assist you with learning complex algorithms, calculations, and coding libraries in a basic and straightforward manner and prepare you for an entry into this hot career path. Here you’ll learn step by step, and in each tutorial, you’ll build up new skills and may improve your understanding of machine learning. If you’ve got some programming or scripting experience, this course clarifies things in a practical and simple to follow strategy that will allow you to understand what you are doing in no time.

Deep Learning

Deep Learning is one of the main aspects to be learned in the field of Data Science. Deep learning is considered as a subset of Machine Learning (ML) which by the way is also a subset of Artificial Intelligence (AI). All these three concepts are co-related to each other, but they are not the same. To be precise with definitions an Artificial Intelligence is a technique that enables a machine to mimic human behaviour. On the other hand, Machine Learning is defined as a technique to achieve AI through algorithms trained with data and finally, Deep Learning is a type of machine learning inspired by the structure of the human brain. In terms of Deep Learning this structure is called an Artificial Neural Network (ANN).

Learn a powerful skill at your home

This is the best course for Machine learning. Theoretical knowledge is not sufficient for machine learning. This course will allow you to practice machine learning concepts every day at home.

Practice makes man perfect and it all depends on your efforts and diligence.

Why Learn From Me

Machine learning course can be challenging and complex. To navigate this tangle, you need a simple and direct approach to the purpose. This course gives you my teaching experience and my knowledge of the industry. I have taught IT for more than seven years to more than 1, 40,000 students, all are happy.

I am also an application developer. Helping you master these issues is my highest priority. My teaching style is different from others and easy to understand because I usually take simple and straightforward examples and follow a step-by-step approach. If you find any difficulty in any video which I even have covered during this course please feel free to ask your doubt. I am always happy to help you.

No question asked – Money Back Guarantee!

There is no risk, this course comes with a 30-day money-back guarantee. Once you purchase the course, if for any reason you are not satisfied with the course, please let me know, I will refund 100%, no questions asked. So you’ve got nothing to lose, check-in for this course, and learn “Machine Learning from A to Z with examples!

At the end of the course, you’ll have great confidence. What are you waiting for?

Join me on this adventure today! See you on the course.
Who this course is for:

Anyone curious about Machine Learning or AI
Students who have a minimum of high school knowledge in math and who need to begin learning Machine Learning
Anyone who isn’t much comfortable with coding but who have an interest in Machine Learning and need to use it easily on datasets
Anyone in college who want to start a career in Data Science
Any data analysts who want to level up in Machine Learning
Software developers or programmers who wants to transition into the machine learning career path will learn a lot from this course

Requirements

Just some high school mathematics level skills will be required
Some prior coding or scripting experience will be required
Basic knowledge of python to kick start your journey in the field of ML

Last Updated 1/2021


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