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A Step-By-Step Journey To Artificial Intelligence

Machine learning (ML) is the study of computer algorithms that develop automatically through experience [1]. According to Wikipedia, machine learning involves computers discovering how to perform tasks without being explicitly programmed [2].ย The first thing that comes to most of you when it comes to artificial intelligence is undoubtedly robots, as you can see in the visual. Today I have researched the relevant courses at the basics of machine learning and artificial intelligence level for you, and here I will list the DataCamp and Coursera courses that I’m most pleased with.

DataCamp Courses


๐Ÿ’  Image Processing with Keras in Python: During this course, CNN networks will be taught how to build, train, and evaluate. It will be taught how to develop learning abilities from data and how to interpret the results of training.
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๐Ÿ’  Preprocessing for Machine Learning in Python:ย  You’ll learn how to standardize your data to be the right format for your model, create new features to make the most of the information in your dataset, and choose the best features to improve your model compliance.
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๐Ÿ’  Advanced Deep Learning with Keras: It shows you how to solve various problems using the versatile Keras functional API by training a network that performs both classification and regression.
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๐Ÿ’  Introduction to TensorFlow in Python:ย In this course, you will use TensorFlow 2.3 to develop, train, and make predictions with suggestion systems, image classification, and models that power significant advances in fintech. You will learn both high-level APIs that will allow you to design and train deep learning models in 15 lines of code, and low-level APIs that will allow you to go beyond ready-made routines.
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๐Ÿ’  Introduction to Deep Learning with PyTorch:ย PyTorch is also one of the leading deep learning frameworks, both powerful and easy to use. In this lesson, you will use Pytorch to learn the basic concepts of neural networks before creating your first neural network to estimate numbers from the MNIST dataset. You will then learn about CNN and use it to build more powerful models that deliver more accurate results. You will evaluate the results and use different techniques to improve them.
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๐Ÿ’  Supervised Learning with scikit-learn:ย 

  • Classification
  • Regression
    • Fine-tuning your model
    • Preprocessing and pipelines

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๐Ÿ’  AI Fundamentals:

  • Introduction to AI
  • Supervised Learning
    • Unsupervised Learning
    • Deep Learning & Beyond

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Coursera Courses


๐Ÿ’  Machine Learning: Classification, University of Washington:ย 

  • The solution of both binary and multi-class classification problems
  • Improving the performance of any model using Boosting
  • Method scaling with stochastic gradient rise
  • Use of missing data processing techniques
  • Model evaluation using precision-recall metrics

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๐Ÿ’  AI For Everyone, deeplearning.ai: ย 

  • Realistic AI can’t be what it can be?
  • How to identify opportunities to apply artificial intelligence to problems in your own organization?
  • What is it like to create a machine learning and data science projects?
  • How does it work with an AI team and build an AI strategy in your company?
  • How to navigate ethical and social discussions about artificial intelligence?

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๐Ÿ’  AI for Medical Diagnosis, deeplearning.ai:ย 

  • In Lesson 1, you will create convolutional neural network image classification and segmentation models to diagnose lung and brain disorders.
  • In Lesson 2, you will create risk models and survival predictors for heart disease using statistical methods and a random forest predictor to determine patient prognosis.
  • In Lesson 3, you will create a treatment effect predictor, apply model interpretation techniques, and use natural language processing to extract information from radiology reports.

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As a priority step in learning artificial intelligence, I took Artificial Neural Networks and Pattern Recognition courses in my Master’s degree. I developed projects related to these areas and had the opportunity to present these projects. So I realized that I added more to myself when I passed on what I knew. In this article, I mentioned the DataCamp and Coursera courses that you should learn in summary. Before this, I strongly recommend that you also finish the Machine Learning Crash Course.

REFERENCES

  1. Mitchell, Tom (1997). Machine Learning. New York: McGraw Hill. ISBNย 0-07-042807-7. OCLCย 36417892.
  2. From Wikipedia, The free encyclopedia, Machine learning, 19 November 2020.
  3. DataCamp, https://learn.datacamp.com.
  4. Coursera, https://www.coursera.org.