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This course is about the learning process of Machine learning which focuses on applications that learn from experience and improve their decision-making or predictive precision over time.

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Unlimited Duration

Last Updated

January 27, 2021

Students Enrolled

Total Video Time

EXPIRED

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Certification

Course Description


Machine learning is the concept that a computer program can learn and adapt to new data without human intervention.

Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. ML is the science of getting computers to learn and act like humans do. 

Machine learning is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being specially programmed. Machine learning research is part of research on artificial intelligence, seeking to provide knowledge to computers through data, observations and interacting with the world. As big data keeps getting bigger as computing becomes more powerful and affordable and the data scientists keep developing. Machine learning will drive greater and greater efficiency in our personal and work lives. Machine learning can be applied in a variety of areas, such as in lending, investing, organizing news, advertising and much more.


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Course Curriculum

    • What is Machine Learning 00:00:00
    • Revolution of Big Data 00:00:00
    • Understand why we need Machine Learning 00:00:00
    • Domains where Machine Learning is applied 00:00:00
    • Supervised Vs Un-supervised Learning 00:00:00
    • Use Cases 00:00:00
    • Simple Linear Regression 00:00:00
    • Multiple Linear Regression 00:00:00
    • OLS 00:00:00
    • Assumptions 00:00:00
    • Gradient Descent 00:00:00
    • Polynomial Regression 00:00:00
    • Train, Test, Validation Split 00:00:00
    • Evaluation Metrics 00:00:00
    • Overfitting, Uderfitting 00:00:00
    • Ridge and Lasso Regression 00:00:00
    • Principal Component Analysis 00:00:00
    • Hands on Regression 00:00:00
    • What is Classification problem 00:00:00
    • Logistic Regression 00:00:00
    • Probability and Odds 00:00:00
    • Log of Odds 00:00:00
    • Maximum Likelihood Estimator 00:00:00
    • Sigmoid Function 00:00:00
    • Confusion Matrix 00:00:00
    • ROC and AUC curves 00:00:00
    • Precision Recall 00:00:00
    • F1-Score 00:00:00
    • Labs 00:00:00
    • What is Decision Tree 00:00:00
    • Entropy and Information Gain 00:00:00
    • Gini Index and Weighted Gini Index 00:00:00
    • Decision Tree for Classification and Regression 00:00:00
    • Overfitting Problem in Decision Tree 00:00:00
    • Labs 00:00:00
    • Types of Ensemble Learning 00:00:00
    • Bagging 00:00:00
    • Types of Boosting Algorithms 00:00:00
    • Ada Boosting 00:00:00
    • Mathematical Intuition behind Ada Boosting 00:00:00
    • Practical Implementation of Ada Boosting 00:00:00
    • Gradient Boosting 00:00:00
    • Mathematical Intuition behind Gradient Boosting 00:00:00
    • Practical Implementation of Gradient Boosting 00:00:00
    • Extreme Gradient Boosting(XGboost) 00:00:00
    • Mathematical Intuition behind XGboost 00:00:00
    • Practical Implementation of XGboost 00:00:00
    • How KKN algorithm works 00:00:00
    • Euclidean and Manhattan Distance 00:00:00
    • Practical Implementation of KNN 00:00:00
    • How to find the best K value 00:00:00
    • Issues to KNN 00:00:00
    • Introduction 00:00:00
    • Mathematical Intuition behind SVM 00:00:00
    • Linear SVM Classifier 00:00:00
    • Hard Margin 00:00:00
    • Soft Margin 00:00:00
    • Practical Implementation 00:00:00
    • Nonlinear SVM Classification 00:00:00
    • Gaussian RBF Kernel 00:00:00
    • Practical Implementation 00:00:00
    • SVM Regression 00:00:00
    • Practical Implementation 00:00:00
    • Conditional Probability 00:00:00
    • Bayes Theorem 00:00:00
    • Naïve Bayes Classifier In-depth Intuition 00:00:00
    • Practical Implementation 00:00:00
    • Cluster Analysis 00:00:00
    • Types of Clustering Algorithms 00:00:00
    • Euclidean and Manhattan Distance 00:00:00
    • Hierarchical Clustering 00:00:00
    • K-Means Clustering 00:00:00
    • DBSCAN Clustering 00:00:00
    • Practical Implementation 00:00:00

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