What You Will Learn!
- Make predictions using linear regression, polynomial regression, and multivariate regression
- Master Machine Learning on Python
- Make robust Machine Learning models
- Have a great intuition of many Machine Learning models
Description
We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
What is Machine learning
Features of Machine Learning
Difference between regular program and machine learning program
Applications of Machine Learning
Types of Machine Learning
What is Supervised Learning
What is Reinforcement Learning
What is Neighbours algorithm
K Nearest Neighbours classification
K Nearest Neighbours Regression
Detailed Supervised Learning
Supervised Learning Algorithms
Linear Regression
Use Case(with Demo)
Model Fitting
Need for Logistic Regression
What is Logistic Regression?
Ridge and lasso regression
Support vector Machines
Pre process of Machine learning data
ML Pipeline
What is Unsupervised Learning
What is Clustering
Types of Clustering
Tree Based Modeles
What is Decision Tree
What is Random Forest
What is Adaboost
What is Gradient boosting
stochastic gradient boostinng
What is Naïve Bayes
Calculation using weather dataset
Entropy Calculation using weather dataset
Trees Entropy and Gini Maths Introduction
Pipeline with SimpleImputer and SVC
Pipeline with feature selection and SVC
Dropping Missing Data
Regression with categorical features using ridge algorithm
processing Categorical Features part2
processing Categorical Features
processing of machine learning data Delete Outliers
processing of machine learning data Outliers
Who Should Attend!
- Any students in college who want to start a career in Data Science.
- Any data analysts who want to level up in Machine Learning.
- Any people who want to create added value to their business by using powerful Machine Learning tools
