Data Science and Machine Learning Using Python

By Bashir Alam Categories: Free courses
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About Course

NOTE: All the content will be added to the course till August 2024: You can still enroll in the course to start learning. 

Welcome to the data science and machine learning course using Python programming language. For this course, you need to have a basic understanding of Python. The course does not require you to be an expert in Python programming but the basic understanding will be fine. If you are new to Python, we recommend you to please take the Intro to Python course first.

The data science and machine learning course is for those who want to gain practical skills in this domain. From data preprocessing to data visualization and from machine learning to neural networks, we will cover every topic with some useful projects. Our main target is to prepare you for jobs related to data science and machine learning.

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What Will You Learn?

  • Data reading using Python
  • Preprocessing
  • Data cleaning
  • Data filtering
  • Data visualization
  • Encoding methods
  • Detect and handle outliers
  • Detect and handle null values
  • Supervised machine learning
  • Unsupervised machine learning
  • Neural networks
  • Dashboards in Python

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.

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.

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.

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.