Video description
This A to Z course introduces newcomers to the world of data science and teaches the fundamental skills for using machine learning and artificial intelligence (AI) to glean meaning and insights from data.
It covers Python’s data types and shows how to use the musthave Python data science libraries, including Pandas for data analysis and Matplotlib for creating visuals of the results. Once you understand how to format and clean your data and perform exploratory data analysis, we move to the machine learning side. Here, we introduce you to supervised vs unsupervised learning, as well as the core algorithms, including simple and multiple linear regression. We finish up with a deep dive into a recommender system for movies, and a chance to put together all your new skills and knowledge.
Each topic is described in plain English, and the course does its best to avoid mathematical notations and jargon. Once you have access to the source code, you can experiment with it and improve upon it, learning and applying these algorithms in the real world.
The data science field is lucrative and growing. This course will introduce you to all the foundational skills that a data scientist must have. If you have no background in statistics, don't let that stop you from enrolling in this course!
Distributed by Manning Publications
This course was created independently by Meta Brains and is distributed by Manning through our exclusive liveVideo platform.
About the Technology
About the Video
What's Inside
 Work with Pandas, a data analysis tool
 How to clean, format, and output data
 Creating plots, charts, and other visuals with Matplotlib
 The differences between supervised and unsupervised machine learning
 The core machine learning algorithms including basic and multiple linear regression
 What is a regression problem and how to tackle it
 The logic behind decision tree and various clustering algorithms
 Building a recommender system for movies
About the Reader
No prior programming or data science knowledge required
About the Author
Meta Brains is a professional training brand developed by a team of software developers and finance professionals who have a passion for coding, finance, and Excel. They bring together both professional and educational experiences to create worldclass training programs accessible to everyone. Currently, they're focused on the next great revolution in computing: The Metaverse. Their ultimate objective is to train the next generation of talent so we can code and build the metaverse together!
Quotes
Table of contents

Course content
 Welcome to the Python for Data Science and ML bootcamp!
 Python: A Brief Overview
 The Python Installation Procedure
 What is Jupyter?
 Set up Anaconda on Different Operating Systems
 How to integrate Python into Jupyter?
 Handling Directories in Jupyter Notebook
 Input and Output
 Working with different datatypes
 Variables
 Arithmetic Operators
 Comparison Operators
 Logical Operators
 Conditional statements
 Loops
 Sequences Part 1: Lists
 Sequences Part 2: Dictionaries
 Sequences Part 3: Tuples
 Functions Part 1: Builtin Functions
 Functions Part 2: Userdefined Functions
 The MustHave Python Data Science Libraries

NumPy Mastery: Everything you need to know about NumPy
 Introduction to NumPy arrays
 Creating NumPy arrays
 Indexing NumPy arrays
 Array shape
 Iterating Over NumPy Arrays
 Basic NumPy arrays: zeros()
 Basic NumPy arrays: ones()
 Basic NumPy arrays: full()
 Adding a scalar
 Subtracting a scalar
 Multiplying by a scalar
 Dividing by a scalar
 Raise to a power
 Transpose
 Elementwise addition
 Elementwise subtraction
 Elementwise multiplication
 Elementwise division
 Matrix multiplication
 Statistics

DataFrames and Series in Python’s Pandas
 What is a Python Pandas DataFrame?
 What is a Python Pandas Series?
 DataFrame vs Series
 Creating a DataFrame using lists
 Creating a DataFrame using a dictionary
 Loading CSV data into python
 Changing the Index Column
 Inplace
 Examining the DataFrame: Head and Tail
 Statistical summary of the DataFrame
 Slicing rows using bracket operators
 Indexing columns using bracket operators
 Boolean list
 Filtering Rows
 Filtering rows using and  operators
 Filtering data using loc()
 Filtering data using iloc()
 Adding and deleting rows and columns
 Sorting Values
 Exporting and saving pandas DataFrames
 Concatenating DataFrames
 groupby()
 Data Cleaning Techniques for Better Data

Exploratory Data Analysis in Python
 Introduction
 What is Exploratory Data Analysis?
 Univariate Analysis
 Univariate Analysis: Continuous Data
 Univariate Analysis: Categorical Data
 Bivariate analysis: Continuous Continuous
 Bivariate analysis: Categorical Categorical
 Bivariate analysis: Continuous Categorical
 Detecting Outliers
 Categorical Variable Transformation
 Python for TimeSeries Analysis: A Primer
 Python for Data Validation: Library, Resources and Sample Graphs
 The Basics of Machine Learning
 Simple Linear Regression with Python
 Multiple Linear Regression with Python
 Classification Algorithms KNearest Neighbors
 Classification Algorithms: Decision Tree
 Classification Algorithms: Logistic regression

Clustering
 Introduction to clustering
 Use cases
 KMeans Clustering Algorithm
 Elbow method
 Steps of the Elbow method
 Implementation in python
 Hierarchical clustering
 Densitybased clustering
 Implementation of kmeans clustering in python
 Importing the dataset
 Visualizing the dataset
 Defining the classifier
 3D Visualization of the clusters
 3D Visualization of the predicted values
 Number of predicted clusters

Recommender System
 Introduction
 Collaborative Filtering in Recommender Systems
 Contentbased Recommender System
 Importing libraries and datasets
 Merging datasets into one dataframe
 Sorting by title and rating
 Histogram showing number of ratings
 Frequency distribution
 Jointplot of the ratings and number of ratings
 Data preprocessing
 Sorting the mostrated movies
 Grabbing the ratings for two movies
 Correlation between the mostrated movies
 Sorting the data by correlation
 Filtering out movies
 Sorting values
 Repeating the process for another movie
 Conclusion
Product information
 Title: Python for Data Science and Machine Learning: Zero to Hero
 Author(s):
 Release date: May 2023
 Publisher(s): Manning Publications
 ISBN: 10000DIVC202341
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