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
  1. In our case, we implemented the linear regression without handling the outliers and the results were not good. Try to first handle the outliers from the dataset and then apply the linear regression model and compare the results. 
  2. Get a dataset from Kaggle and make sure this dataset has multiple input columns. Try implementing the Linear Regression model on a multi column dataset. 
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