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Artificial Intelligence Bootcamp 44 projects Ivy League pro

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Artificial Intelligence Bootcamp 44 projects Ivy League pro, available at $199.99, has an average rating of 3.8, with 74 lectures, based on 80 reviews, and has 13557 subscribers.

You will learn about Code for image recognition, handwriting recognition, data analysis, and create recurrent neural networks. This course is ideal for individuals who are Beginning to Pro Python Developers who want to get started using Machine Learning in a realistic way using numerical or image data sets. It is particularly useful for Beginning to Pro Python Developers who want to get started using Machine Learning in a realistic way using numerical or image data sets.

Enroll now: Artificial Intelligence Bootcamp 44 projects Ivy League pro

Summary

Title: Artificial Intelligence Bootcamp 44 projects Ivy League pro

Price: $199.99

Average Rating: 3.8

Number of Lectures: 74

Number of Published Lectures: 74

Number of Curriculum Items: 74

Number of Published Curriculum Objects: 74

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Code for image recognition, handwriting recognition, data analysis, and create recurrent neural networks.

Who Should Attend

  • Beginning to Pro Python Developers who want to get started using Machine Learning in a realistic way using numerical or image data sets.

Target Audiences

  • Beginning to Pro Python Developers who want to get started using Machine Learning in a realistic way using numerical or image data sets.

My name is Gopal.  I used AI to classify brain tumors.  I have 11 publications on pubmed talking about that.  I went to Cornell University and taught at Cornell, Amherst and UCSF.  I worked at UCSF and 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 dominant language in intelligence and machine learning programming 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 you to 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).  ALL THE CODE IS INCLUDED AND EASY TO EXECUTE.  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.  

I use 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 

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Installing Anaconda

Chapter 2: Statistics Projects

Lecture 1: Statistics Projects Introduction

Lecture 2: Statistics Projects 1.DescriptiveStatistics

Lecture 3: Statistics Projects 2.ClassicalInferenceProportions

Lecture 4: Statistics Projects 3.ClassicalInferenceMeans

Chapter 3: Bayesian Projects

Lecture 1: Bayesian Projects Intro

Lecture 2: Bayesian Projects 4.BayesianAnalysis

Lecture 3: Bayesian Projects 5.BayesianInferenceProportions

Lecture 4: Bayesian Projects 6.BayesianInferenceMeans

Lecture 5: Bayesian Projects 7.Correlations

Chapter 4: Machine Learning

Lecture 1: Machine Learning Intro

Lecture 2: Machine Learning Introduction 8.MachineLearningPrinciples

Lecture 3: Machine Learning Introduction 9.TrainingMLModes

Lecture 4: Machine Learning Introduction 10.EvaluatingModelResults

Chapter 5: Deep Learning Projects

Lecture 1: Deep Learning Introduction

Lecture 2: Deep Learning Projects 11.knn

Lecture 3: Deep Learning Projects 12.DecisionTree

Lecture 4: Deep Learning Projects 13.RandomForests

Lecture 5: Deep Learning Projects 14.OLS

Lecture 6: Deep Learning Projects 15.EvaluatingLinearModels

Lecture 7: Deep Learning Project 17.LASSORegression

Lecture 8: Deep Learning Projects 18.Interpolation

Lecture 9: Deep Learning Projects 19.Perceptron

Lecture 10: Deep Learning Projects 20.TrainingNeuralNetwork

Lecture 11: Deep Learning Projects 21.RegressionNeuralNetwork

Lecture 12: Deep Learning Projects 22. Clustering

Lecture 13: Deep Learning Projects 23. Evaluative Clustering

Lecture 14: Deep Learning Projects 24.kmeans

Lecture 15: Deep Learning Projects 25.Hierarchical

Lecture 16: Deep Learning Projects 26. Spectral

Lecture 17: Deep Learning Projects 27.PCA

Lecture 18: Deep Learning Projects 28.SVD

Lecture 19: Deep Learning Projects 29. Low Dimensional

Chapter 6: Machine Learning AI

Lecture 1: Machine Learning AI Intro

Lecture 2: Machine Learning AI 1.Machine Learning

Lecture 3: Machine Learning AI 2.Training Algorithms Part 1

Lecture 4: Machine Learning AI 2.Training Algorithms Part 2

Lecture 5: Machine Learning AI 3.SciKit Part 1

Lecture 6: Machine Learning AI 3.SciKit Part 2

Lecture 7: Machine Learning AI 3.SciKit Part 3

Lecture 8: Machine Learning AI 4.Data Pre Processing Part 1

Lecture 9: Machine Learning AI 4.Data Pre Processing Part 2

Lecture 10: Machine Learning AI 4.Data Pre Processing Part 3

Lecture 11: Machine Learning AI 5.Dimentionality Reduction Part 1

Lecture 12: Machine Learning AI 5.Dimentionality Reduction Part 2

Lecture 13: Machine Learning AI 5.Dimentionality Reduction Part 3

Lecture 14: Machine Learning AI 6.Hyperparameter Optimization Part 1

Lecture 15: Machine Learning AI 6.Hyperparameter Optimization Part 2

Lecture 16: Machine Learning AI 6.Hyperparameter Optimization Part 3

Lecture 17: Machine Learning AI 7. Ensemble Learning Part 1

Lecture 18: Machine Learning AI 7. Ensemble Learning Part 2

Lecture 19: Machine Learning AI 8.Sentiment Analysis Part 1

Lecture 20: Machine Learning AI 8.Sentiment Analysis Part 2

Lecture 21: Machine Learning AI 9.Regression Analysis Part 1

Lecture 22: Machine Learning AI 9.Regression Analysis Part 2

Lecture 23: Machine Learning AI 9.Regression Analysis Part 3

Lecture 24: Machine Learning AI 10.Cluster Analysis Part 1

Lecture 25: Machine Learning AI 10.Cluster Analysis Part 2

Lecture 26: Machine Learning AI 11.Artificial Neural Networks Part 1

Lecture 27: Machine Learning AI 11.Artificial Neural Networks Part 2

Lecture 28: Machine Learning AI 12.TensorFlow Part 1

Lecture 29: Machine Learning AI 12.TensorFlow Part 2

Lecture 30: Machine Learning AI 12.TensorFlow Part 3

Lecture 31: Machine Learning AI 13.TensorFlow Workshop Part 1

Lecture 32: Machine Learning AI 13.TensorFlow Workshop Part 2

Lecture 33: Machine Learning AI 13.TensorFlow Workshop Part 3

Lecture 34: Machine Learning AI 14.CNN for Images Part 1

Lecture 35: Machine Learning AI 14.CNN for Images Part 2

Lecture 36: Machine Learning AI 14.CNN for Images Part 3

Lecture 37: Machine Learning AI 15.Recurrent Neural Network Part 1

Lecture 38: Machine Learning AI 15.Recurrent Neural Network Part 2

Lecture 39: Machine Learning AI 15.Recurrent Neural Network Part 3

Lecture 40: Machine Learning AI 15.Recurrent Neural Network Part 4

Instructors

Rating Distribution

  • 1 stars: 4 votes
  • 2 stars: 5 votes
  • 3 stars: 18 votes
  • 4 stars: 30 votes
  • 5 stars: 23 votes

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