3.80 (78 reviews)

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What you will learn

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

Description

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

Screenshots

Content

Introduction

Introduction

Installing Anaconda

Statistics Projects

Statistics Projects Introduction

Statistics Projects 1.DescriptiveStatistics

Statistics Projects 2.ClassicalInferenceProportions

Statistics Projects 3.ClassicalInferenceMeans

Bayesian Projects

Bayesian Projects Intro

Bayesian Projects 4.BayesianAnalysis

Bayesian Projects 5.BayesianInferenceProportions

Bayesian Projects 6.BayesianInferenceMeans

Bayesian Projects 7.Correlations

Machine Learning

Machine Learning Intro

Machine Learning Introduction 8.MachineLearningPrinciples

Machine Learning Introduction 9.TrainingMLModes

Machine Learning Introduction 10.EvaluatingModelResults

Deep Learning Projects

Deep Learning Introduction

Deep Learning Projects 11.knn

Deep Learning Projects 12.DecisionTree

Deep Learning Projects 13.RandomForests

Deep Learning Projects 14.OLS

Deep Learning Projects 15.EvaluatingLinearModels

Deep Learning Project 17.LASSORegression

Deep Learning Projects 18.Interpolation

Deep Learning Projects 19.Perceptron

Deep Learning Projects 20.TrainingNeuralNetwork

Deep Learning Projects 21.RegressionNeuralNetwork

Deep Learning Projects 22. Clustering

Deep Learning Projects 23. Evaluative Clustering

Deep Learning Projects 24.kmeans

Deep Learning Projects 25.Hierarchical

Deep Learning Projects 26. Spectral

Deep Learning Projects 27.PCA

Deep Learning Projects 28.SVD

Deep Learning Projects 29. Low Dimensional

Machine Learning AI

Machine Learning AI Intro

Machine Learning AI 1.Machine Learning

Machine Learning AI 2.Training Algorithms Part 1

Machine Learning AI 2.Training Algorithms Part 2

Machine Learning AI 3.SciKit Part 1

Machine Learning AI 3.SciKit Part 2

Machine Learning AI 3.SciKit Part 3

Machine Learning AI 4.Data Pre Processing Part 1

Machine Learning AI 4.Data Pre Processing Part 2

Machine Learning AI 4.Data Pre Processing Part 3

Machine Learning AI 5.Dimentionality Reduction Part 1

Machine Learning AI 5.Dimentionality Reduction Part 2

Machine Learning AI 5.Dimentionality Reduction Part 3

Machine Learning AI 6.Hyperparameter Optimization Part 1

Machine Learning AI 6.Hyperparameter Optimization Part 2

Machine Learning AI 6.Hyperparameter Optimization Part 3

Machine Learning AI 7. Ensemble Learning Part 1

Machine Learning AI 7. Ensemble Learning Part 2

Machine Learning AI 8.Sentiment Analysis Part 1

Machine Learning AI 8.Sentiment Analysis Part 2

Machine Learning AI 9.Regression Analysis Part 1

Machine Learning AI 9.Regression Analysis Part 2

Machine Learning AI 9.Regression Analysis Part 3

Machine Learning AI 10.Cluster Analysis Part 1

Machine Learning AI 10.Cluster Analysis Part 2

Machine Learning AI 11.Artificial Neural Networks Part 1

Machine Learning AI 11.Artificial Neural Networks Part 2

Machine Learning AI 12.TensorFlow Part 1

Machine Learning AI 12.TensorFlow Part 2

Machine Learning AI 12.TensorFlow Part 3

Machine Learning AI 13.TensorFlow Workshop Part 1

Machine Learning AI 13.TensorFlow Workshop Part 2

Machine Learning AI 13.TensorFlow Workshop Part 3

Machine Learning AI 14.CNN for Images Part 1

Machine Learning AI 14.CNN for Images Part 2

Machine Learning AI 14.CNN for Images Part 3

Machine Learning AI 15.Recurrent Neural Network Part 1

Machine Learning AI 15.Recurrent Neural Network Part 2

Machine Learning AI 15.Recurrent Neural Network Part 3

Machine Learning AI 15.Recurrent Neural Network Part 4

Reviews

p

prince30 May 2019

Instructor doesn't teach all the things like in classicalInterferenceproportions he doesn't tell about numpy,pandas etc.

R

Rolf28 May 2019

Terrible introduction (without any "welcome to this new course "Artificial Intelligence Bootcamp 44 projects Ivy League pro") with no explanations about what this course is all about. This course should be removed from Udemy as it does not look very professional nor appealing!

J

Jay17 September 2018

Awful, instructor just reads notes when he himself doesn't actually understand what is going on. Don't enroll, waste of time.

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