Course Content
Data Reading using Python
As a data scientist we should be able to read different types of datasets. In this lesson, we will cover how to read dataset in different formats using Python and its various modules.
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Read datasets using Pandas
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Data Preprocessing
In this section, we will cover various methods that can help us to clean the dataset and make it suitable for a machine learning model. In general, we will cover the basic methods that every data scientist should know. We will learn about encoding, outliers, null values, and hundling imbalance datasets. By the end of this section, you will be comfortable to preprocess the dataset and make it clean.
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Encoding Methods in Data Science Using Pandas
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Practice Questions
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Detect and hundle missing values in Python
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Practice Questions
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Detect and handle outliers in Machine Learning
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Practice Questions
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Project-1: Data Analysis Project
Welcome to the first project! This is going to be a very simple project which is about data analysis. Your task is to import the raw dataset and apply various methods to analyze and find the hidden trends from the dataset. We have already provided the solution as well, but you are recommended to try the project first by yourself and then look at the attached file for the solution.
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Description of Project
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Supervised Machine Learning
A machine learning is actually using some models to go through our dataset to find the trends and information from our data automatically. The machine learning can be superivsed or unsupervised. The supervised machine learning is when you have the target variable in our your dataset or you have a labeled dataset. The supervised machine learning will find the relation between the input data and the target variable and will use this relation to make prediction later. In this section, we will go through various kinds of supervised machine learning models and will analyze them.
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What is Supervised Machine Learning?
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Linear Regression Model in Supervised Learning
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Practice Questions
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KNN Algorithm
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Data Science and Machine Learning Using Python
About Lesson

Machine Learning is training a model to understand the dataset and help us to find the hidden trends and information from our dataset. When it comes to machine learning, there are three main types. 

  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Reinforcement Learning

In this lesson, we will discuss supervised learning and will continue with the other types in our upcoming lessons. 

What is Supervised Learning?

Supervised Machine Learning is a type of learning that needs a labeled dataset. A labeled dataset is data where we have a target column specified. Examples of labeled datasets include data about house prices, stock markets, temperature, etc. In other words, when we know exactly what the prediction of the model should be, we can use supervised machine learning. 

In the case of supervised machine learning, we will simply give the input values and the target column to the model for the training. The training of supervised machine learning is the process when the model will try to find the relation between the input datasets and the target columns and use this relation later to make predictions. 

Types of Supervised Machine Learning

Supervised Machine Learning has two main types. 

  • Classification 
  • Regression

We use the classification models when the data is a classification dataset. A classification dataset is a dataset that has categorical values in the target column. Examples of classification datasets include t-shirt sizes, grades of students, gender, etc. 

On the other hand, regression models are applied to a regression dataset. A dataset that has continuous values in the target column is known as a regression dataset. Examples of regression datasets include temperature, stock market, house price, age, etc. 

In the upcoming lessons, we will discuss various models from each of these sections. 

Classification Models

  • KNN
  • Logistic Regression
  • SVM
  • Naive Bayes

Regression Models

  • Linear Regression 
  • Extra Tree Regressors

Models for Both Classification and Regression

  • Decision Trees
  • Random Forest
  • XGboost
  • LightGBM
  • CatBoost
  • AdaBoost

Let us now go through the implementation of each of these models. 

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