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Python Machine Learning and Data Science Course

person icon Abhilash Nelson

3.9

Python Machine Learning and Data Science Course

Machine Learning and Data Science for programming beginners using Python with scikit-learn, SciPy, Matplotlib, and Pandas

updated on icon Updated on Apr, 2024

language icon Language - English

person icon Abhilash Nelson

English [CC]

category icon Development,Python,Machine Learning

Lectures -92

Resources -2

Duration -10 hours

3.9

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Course Description

Python Machine Learning and Data Science are crucial and this course will walk you through them. Machine Learning, Artificial Intelligence, and Deep Learning neural networks are the most frequently used terms and also the most muddled and misunderstood terms. Neural networks and machine learning are two subsets of the broad machine learning platform.

Python Machine Learning and Data Science for Beginners Overview

When we were young, we began to think logically about a variety of topics and to experience emotions, among other things. We persisted in thinking and came up with fixes for issues we encountered every day. The scientists working on deep learning neural networks are aiming towards that. a device that thinks.

Nonetheless, the primary area of focus in this course is machine learning. We are getting our machine ready for a prediction test during this course. It works exactly like how you would prepare for a maths exam in school or college. We develop our skills and practice ourselves by resolving as many similar mathematical puzzles as we can. Let's refer to these hypothetical examples of comparable issues and their solutions as the "Training Input" and "Training Output," respectively. And then the day of the test finally arrives. We will be given a brand-new set of issues to tackle, but they will be quite similar to the problems we learned. We must solve them based on our prior practice and learning experiences.

These issues might be referred to as "Testing Input" and our solutions as "Predicted Output." These responses will then be evaluated by our professor and compared to the actual responses, which we refer to as the "Test Output." Following that, a grade will be assigned based on the correct responses. We refer to this as our "Accuracy" mark. A machine learning engineer's and data scientist's livelihood is devoted to using various methods and evaluation criteria to increase this accuracy as much as feasible.

These are the main subjects covered in this course. Python is the programming language we are employing. Python is a fantastic tool for creating programs that analyze and predict data. It offers a ton of classes and features that carry out intricate mathematical analyses and provide solutions in straightforward one or two lines of code, allowing anyone to learn data science and machine learning without needing to be an expert statistician or mathematician. Python considerably streamlines the process.

Goals

What will you learn in this course:

  • Beginners who are interested in Machine Learning using Python

  • Learn fundamentals of machine learning and data science using Python

  • Develop the skills you need to apply machine learning and data science to real-world problems

  • Prepare for a career in machine learning or data science

  • Understand the scikit-learn machine learning library

  • Learn how data visualization works

  • Understand how natural language processing works

  • Understand how deep learning works

Prerequisites

What are the prerequisites for this course?

  • A medium-configuration computer and the willingness to indulge in the world of Machine Learning
Python Machine Learning and Data Science Course

Curriculum

Check out the detailed breakdown of what’s inside the course

Introduction
3 Lectures
  • play icon Course Overview & Table of Contents 09:08 09:08
  • play icon Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types 04:37 04:37
  • play icon Introduction to Machine Learning - Part 2 - Classifications and Applications 05:54 05:54
System and Environment preparation
3 Lectures
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Learn Basics of python
4 Lectures
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Learn Basics of NumPy
3 Lectures
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Learn Basics of Matplotlib
1 Lectures
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Learn Basics of Pandas
2 Lectures
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CSV data file
4 Lectures
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Dataset Summary
4 Lectures
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Dataset Visualization
2 Lectures
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Multivariate Dataset Visualization
3 Lectures
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Data Preparation (Pre-Processing)
7 Lectures
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Feature Selection
6 Lectures
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Refresher Session - The Mechanism of Re-sampling, Training and Testing
1 Lectures
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Algorithm Evaluation Techniques
5 Lectures
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Algorithm Evaluation Metrics
10 Lectures
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Classification Algorithm Spot Check
13 Lectures
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Compare Algorithms
2 Lectures
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Pipelines
2 Lectures
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Performance Improvement
5 Lectures
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Export, Save and Load Machine Learning Models
2 Lectures
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Finalizing Model
2 Lectures
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Quick Session: Imbalanced Data Set - Issue Overview And Steps
1 Lectures
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Iris Dataset : Finalizing Multi-Class Dataset
1 Lectures
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Finalizing a Regression Model - The Boston Housing Price Dataset
1 Lectures
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Real-time Predictions
3 Lectures
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SOURCE CODE ATTACHED
1 Lectures
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Instructor Details

Abhilash Nelson

Abhilash Nelson

e


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Jaaman Dunker

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very comprehensive course.

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