What will you learn in this course?

Develop efficient AI algorithms using C programming language for machine learning and artificial intelligence applications
Implement machine learning models and neural networks with C for real-world AI solutions
Optimize AI and machine learning code performance in C to handle large datasets and complex computations
Apply C programming skills to develop AI-driven applications, including data analysis and predictive modeling

Utilize C language features to enhance the accuracy and efficiency of machine learning algorithms
Integrate AI and machine learning libraries with C for advanced data processing and automation
Troubleshoot and debug AI and machine learning programs written in C for robust application development
Design scalable AI systems using C to support real-time data processing and intelligent decision-making

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Lessons | 15

01:21:14

AI ML Lecture 1 Introduction to Artificial Intelligence AI and Machine Learning ML

01:51:48

AI ML Lecture 2 Introduction to Machine Learning Decision Trees Supervised Learning

01:57:17

AI ML Lecture 3 Practical Example of Decision Trees with C and Accord NET Cross Validation

01:38:40

AI ML Lecture 4 Proper Model Training Testing KNN Algorithm Practical Example Accuracy

01:57:13

AI ML Lecture 5 Learning a Linear Classifier Perceptron Learning Hyperplanes KNN Neural

03:11:39

AI ML Lecture 6 A Real Full Practical Example of How to Do Multilayer Perceptron Training 1 2

02:26:08

AI ML Lecture 7 A Real Full Practical Example of How to Do Multilayer Perceptron Training 2 2

02:10:02

AI ML Lecture 8 Feature Selection Normalization Data Pre Processing TF IDF Text Processing

01:48:43

AI ML Lecture 9 Supervised Evaluation K Fold Cross Validation Multiclass Classification

01:20:19

AI ML Lecture 10 What Is Learning To Rank LTR Pointwise Pairwise and Listwise Ranking

01:14:37

AI ML Lecture 11 Gradient Descent Loss Function Sparse Missing Data Regularization L1 L2

00:57:49

AI ML Lecture 12 Large Soft Margin Classifiers Support Vector Machines SVM Loss Function

01:29:25

AI ML Lecture 13 Conditional Probability Probabilistic Models Joint Distribution Random Var

00:44:24

AI ML Lecture 14 Logistic Regression Ensemble Learning Bagging Boosting AdaBoost

01:46:25

AI ML Lecture 15 Unsupervised Learning Clustering Algorithms Hierarchical Clustering K Means

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Swarali Inchure

Good

2024-05-17

Lakshyan Alahari

very detailed and very informative and like to learn more

2024-02-03

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Any programming language will do. Sometimes special purpose languages will help, but most of the time you need a generic programming language to do complex things. So as a programming language (for AI), C# is as good as Java, or maybe even as good as C and C++ .

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How to Get The Certificate

AI ML Lecture 1 Introduction to Artificial Intelligence AI and Machine Learning ML

AI ML Lecture 2 Introduction to Machine Learning Decision Trees Supervised Learning

AI ML Lecture 3 Practical Example of Decision Trees with C and Accord NET Cross Validation

AI ML Lecture 4 Proper Model Training Testing KNN Algorithm Practical Example Accuracy

AI ML Lecture 5 Learning a Linear Classifier Perceptron Learning Hyperplanes KNN Neural

AI ML Lecture 6 A Real Full Practical Example of How to Do Multilayer Perceptron Training 1 2

AI ML Lecture 7 A Real Full Practical Example of How to Do Multilayer Perceptron Training 2 2

AI ML Lecture 8 Feature Selection Normalization Data Pre Processing TF IDF Text Processing

AI ML Lecture 9 Supervised Evaluation K Fold Cross Validation Multiclass Classification

AI ML Lecture 10 What Is Learning To Rank LTR Pointwise Pairwise and Listwise Ranking

AI ML Lecture 11 Gradient Descent Loss Function Sparse Missing Data Regularization L1 L2

AI ML Lecture 12 Large Soft Margin Classifiers Support Vector Machines SVM Loss Function

AI ML Lecture 13 Conditional Probability Probabilistic Models Joint Distribution Random Var

AI ML Lecture 14 Logistic Regression Ensemble Learning Bagging Boosting AdaBoost

AI ML Lecture 15 Unsupervised Learning Clustering Algorithms Hierarchical Clustering K Means

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