Learn Complete Machine Learning Bootcamp with Python. Build 5 Complete Machine Learning Real World Projects with Python.
What you will learn from this Course:
- 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
Requirements for this Course:
- To make sense out of this course, you should be well aware of linear algebra, calculus, statistics, probability and python programming language.
Wild about Data Science and Machine Learning?
This course is an ideal fit for you.
This course will make you stride by venture into the universe of Machine Learning.
AI is the investigation of PC calculations that computerizes scientific model structure. It is a part of Artificial Intelligence dependent on the possibility that frameworks can gain from information, recognize examples and settle on choices with negligible human intercession.
AI is effectively being utilized today, maybe in a lot a bigger number of spots than one world anticipates.
It contains a ton of points and this course will cover all bit by bit.
This Machine Learning course will give you hypothetical just as useful information on Machine Learning.
This Machine Learning course is fun just as invigorating.
It will cover all normal and significant calculations and will give you the experience of dealing for certain genuine tasks.
This course will cover the accompanying points:-
- Hypothesis and viable execution of direct relapse utilizing sklearn.
- Hypothesis and viable execution of strategic relapse utilizing sklearn.
- Element determination utilizing RFECV.
- Information change with direct and strategic relapse.
- Assessment measurements to investigate the exhibition of models
- Industry significance of direct and strategic relapse.
- Science behind KNN, SVM, and Naive Bayes calculations.
- Execution of KNN, SVM, and Naive Bayes utilizing sklearn.
- Quality determination techniques Gini Index and Entropy.
- Science behind Decision trees and irregular backwoods.
- Boosting calculations:- Adaboost, Gradient Boosting, and XgBoost.
- Various calculations for bunching
- Various strategies to manage imbalanced information.
- Connection sifting
- Change sifting
- PCA and LDA
- Content and Collaborative based sifting
- Particular Value disintegration
- Various calculations utilized for Time Series guaging.
- Contextual investigations
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.
- Python Fundamentals
- Mastering Python Data Structures
- Python Functions Deep Drive
- Python For Data Science
- Data Cleaning
- Data Visualization
- Feature Engineering
- Data Processing
- Linear Regression
- Logistic Regression
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