English | Size: 20.82 GB
Genre: eLearning
What you’ll learn
Theory and practical implementation of linear regression using sklearn
Theory and practical implementation of logistic regression using sklearn
Feature selection using RFECV
Data transformation with linear and logistic regression.
Evaluation metrics to analyze the performance of models
Industry relevance of linear and logistic regression
Mathematics behind KNN, SVM and Naive Bayes algorithms
Implementation of KNN, SVM and Naive Bayes using sklearn
Attribute selection methods- Gini Index and Entropy
Mathematics behind Decision trees and random forest
Boosting algorithms:- Adaboost, Gradient Boosting and XgBoost
Different Algorithms for Clustering
Different methods to deal with imbalanced data
Correlation Filtering
Variance Filtering
PCA & LDA
Content and Collaborative based filtering
Singular Value Decomposition
Different algorithms used for Time Series forecasting
Case studies
Hands on Real-World examples.
Who this course is for:
Anyone who want to start a career in Machine Learning.
Students who have at least knowledge in linear algebra, calculus, statistics, probability and who want to start their journey in Machine Learning.
Any people who want to level up their Machine Learning Knowledge.
Software developers or programmers or Tech lover who want to change their career path to machine learning.
Technologists who are curious about how Machine Learning works in the real world.
Anyone who has already started their data science journey and now want to master in machine learning.
If you have no prior coding or scripting experience, This course is completely for you. This Course also includes Python Fundamental for beginners.
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