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Understand The Machine Learning From Scratch For Beginners
- What is Machine learning ?
- What are Real Scenarios where ML is Used?
- Types of ML Algorithm?
- Difference between Supervised Learning, Unsupervised Learning?
- Steps Involved In Machine Learning?
- List Of Common Machine Learning Algorithm
- Supervised Learning(Task Driven)
- Unsupervised Learning(Data Driven)
- Reinforcement Learning(Learning From Environment)

- suppose you had a basket and it is fulled with some fresh fruits your task is to arrange the same type fruits at one place.
- suppose the fruits are apple,banana,cherry,grape.
- so you already know from your previous work that, the shape of each and every fruit so it is easy to arrange the same type of fruits at one place.here your previous work is called as train data in data mining.
- so you already learn the things from your train data, This is because of you have a response variable which says you that if some fruit have so and so features it is grape, like that for each and every fruit.
- This type of data you will get from the train data.
- This type of learning is called as supervised learning.
- So you already learn the things so you can do you job confidently.
- This time you don't know any thing about that fruits, you are first time seeing these fruits so how will you arrange the same type of fruits.
- What you will do first you take on fruit and you will select any physical character of that particular fruit. suppose you taken color.
- Then you will arrange them base on the color, then the groups will be some thing like this.
- RED COLOR GROUP: apples & cherry fruits.
- GREEN COLOR GROUP: bananas & grapes.

- Collecting data:
- Preparing the data
- Training a model: This step involves choosing the appropriate algorithm and representation of data in the form of the model. The cleaned data is split into two parts�¢??-�¢??train and test (proportion depending on the prerequisites); the first part (training data) is used for developing the model. The second part (test data), is used as a reference.
- Evaluating the model
- Improving the performance.
- Linear Regression
- Logistic Regression
- Decision Tree
- SVM
- Naive Bayes
- KNN
- Random Forest
- Dimensionality Reduction Algorithms
- Gradient Boost & Ada boost
- Time Series Modelling