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Artificial Intelligence Bootcamp 44 projects Ivy League pro

via Udemy


Be a Machine Learning, Matplotlib, NumPy, and TensorFlow pro. Use AI for programming, business or science!

What you'll learn:
  • Code for image recognition, handwriting recognition, data analysis, and create recurrent neural networks.

My name is Gopal. Iused AI to classify brain tumors. Ihave 11 publications on pubmed talking about that. I went to Cornell University and taught at Cornell, Amherst and UCSF. I worked at UCSFand NIH.

AI and Data Science are taking over the world! Well sort of, and not exactly yet. This is the perfect time to hone you skills in AI,data analysis, and robotics, Artificial Intelligence has taken the world by storm as a major field of research and development. Python has surfaced as the dominantlanguage in intelligence and machine learningprogramming because of its simplicity and flexibility, in addition to its great support for open source libraries and TensorFlow.

This video course is built for those with a NO understanding of artificial intelligence or Calculus and linear Algebra. We will introduce youto advanced artificial intelligence projects and techniques that are valuable for engineering, biological research, chemical research, financial, business, social, analytic, marketing (KPI), and so many more industries. Knowing how to analyze data will optimize your time and your money. There is no field where having an understanding of AI will be a disadvantage. AI really is the future.

We have many projects, such natural language processing , handwriting recognition, interpolation, compression, bayesian analysis, hyperplanes (and other linear algebra concepts). ALLTHECODEISINCLUDED ANDEASYTOEXECUTE. You can type along or just execute code in Jupyter if you are pressed for time and would like to have the satisfaction of having the course hold your hand.

Iuse the AI I created in this course to trade stock. You can use AI to do whatever you want. These are the projects which we cover.

For Data Science / Machine Learning / Artificial Intelligence

  • 1. Machine Learning

  • 2. Training Algorithm

  • 3. SciKit

  • 4. Data Preprocessing

  • 5. Dimesionality Reduction

  • 6. Hyperparemeter Optimization

  • 7. Ensemble Learning

  • 8. Sentiment Analysis

  • 9. Regression Analysis

  • 10.Cluster Analysis

  • 11. Artificial Neural Networks

  • 12. TensorFlow

  • 13. TensorFlow Workshop

  • 14. Convolutional Neural Networks

  • 15. Recurrent Neural Networks

Traditional statistics and Machine Learning

  • 1. Descriptive Statistics

  • 2.Classical Inference Proportions

  • 3. Classical InferenceMeans

  • 4. Bayesian Analysis

  • 5. Bayesian Inference Proportions

  • 6. Bayesian Inference Means

  • 7. Correlations

  • 11. KNN

  • 12. Decision Tree

  • 13. Random Forests

  • 14. OLS

  • 15. Evaluating Linear Model

  • 16. Ridge Regression

  • 17. LASSO Regression

  • 18. Interpolation

  • 19. Perceptron Basic

  • 20. Training Neural Network

  • 21. Regression Neural Network

  • 22. Clustering

  • 23. Evaluating Cluster Model

  • 24. kMeans

  • 25. Hierarchal 26. Spectral

  • 27. PCA

  • 28. SVD

  • 29. Low Dimensional

Taught by

Gopal Shangari

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