Published 2/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.44 GB | Duration: 3h 51m
and expand your skills
What you’ll learn
Learners will become familiar with Python keywords
Learners will become familiar with Python functions
Learners will become familiar with the numpy library
Learners will become familiar with the matplotlib library
Learners will become familiar with the seaborn library
Learners will become familiar with the seaborn library
Learners will become familiar with the sklearn library
Learners will be exposed to two machine learning projects; one being a classification model and the second being a regression model
Requirements
Learners must have taken my free Introduction to Python course.
Description
Machine learning is a subset of artificial intelligence that allows computer systems to automatically learn and improve from experience without being explicitly programmed to do so. It involves the use of algorithms that can analyze data, identify patterns, and make predictions or decisions based on the data.In machine learning, the computer system is trained on a large dataset of input and output pairs to learn a model or pattern that can predict the output given a new input. The training process involves adjusting the model’s parameters to minimize errors or maximize accuracy in the predictions.There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. This course will focus on supervised learning, where the system is trained on labeled data, meaning the input data is accompanied by corresponding output labels. Python is one programming language that data scientists use to carry out machine learning activities. Python is a high-level, interpreted programming language that is widely used for various purposes, including web development, scientific computing, data analysis, artificial intelligence, and more. It was created in the late 1980s by Guido van Rossum and has since become one of the most popular programming languages in the world.Python is known for its simplicity and readability, making it easy for beginners to learn and use. Its syntax is straightforward and uses indentation to define code blocks, which helps to reduce the amount of syntax required to write a program.Python also has a vast standard library that provides many useful modules for performing various tasks such as file I/O, networking, web development, and more. It also has a large and active community of developers who contribute to numerous third-party libraries that extend its capabilities.Overall, Python is a versatile and powerful programming language that can be used for a wide range of applications. Its simplicity, readability, and large community make it an excellent choice for beginners and professionals alike.This course will focus on how the python programming language relates to machine learning. During the course, the student will learn:-1. The 33 keywords in Python.2. The 68 functions in Python.3. Become familiar with numpy, Python’s numerical computing library.4. Become familiar with matplotlib, a data visualisation library.5. Become familiar with seaborn, a data visualisation library written on top of matplotlib.6. Become familiar with pandas, Python’s data processing library.7. Become familiar with sklearn, Python’s machine learning library.
Overview
Section 1: Introduction
Lecture 1 Introduction to course
Section 2: Lessons
Lecture 2 Python keywords
Lecture 3 Python functions
Lecture 4 Numpy 1
Lecture 5 Numpy2
Lecture 6 Matplotlib
Lecture 7 Seaborn
Lecture 8 Pandas 1
Lecture 9 Pandas 2
Lecture 10 Sklearn
Section 3: Projects
Lecture 11 Classification problem
Lecture 12 Regression problem
This course is intended for beginner Python developers who have an interest in machine learning.
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