# Python for Data Science & Machine Learning: Zero to Hero

Master Data Science & Machine Learning in Python: Numpy, Pandas, Matplotlib, Scikit-Learn, Machine Learning, and more!

**Language**: english

**Note**: 4.4/5 (72 notes) 9,379 students

**Instructor(s)**: Meta Brains

**Last update**: 2022-10-30

## What you’ll learn

- Gain familiarity with Pandas, a data analysis tool
- Get a grasp on the theory behind basic and multiple linear regression
- Tackle regression problems easily
- Discover the logic behind decision trees
- Acquaint yourself with the various clustering algorithms

## Requirements

- The ability to do simple math
- No programming experience needed
- No prior data science knowledge required
- Readiness, flexibility, and passion for learning

## Description

This machine learning course will provide you the fundamentals of how companies like Google, Amazon, and even Udemy utilize machine learning and artificial intelligence (AI) to glean **meaning and insights from massive data sets**. Glassdoor and Indeed both report that the average salary for a data scientist is $120,000. This is the standard, not the exception.

Data scientists are already quite desirable. It’s difficult to keep them on staff in today’s tight labor market. **There is a severe shortage of people who possess the rare combination of scientific training, computer expertise, and analytical talents**.

Today’s data scientists are held to the same standards as the Wall Street “quants” of the ’80s and ’90s. When the need arose for innovative algorithms and data approaches, physicists and mathematicians flocked to investment banks and hedge funds.

So, it’s no surprise that **data science is rising to prominence **as a promising career path in the modern day. It is analytic in focus, driven by code, and performed on a computer. As a result, it shouldn’t be a shock that the demand for data scientists has been growing steadily in the workplace for the past few years.

On the other hand, availability has been low. Obtaining the education and experience necessary to be hired as a data scientist is tough. And that’s why we made this course in the first place!

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, rather than in a theoretical or academic setting, is the focus of this course.

Each video will leave you with a new perspective that you can implement right away!

If you have no background in statistics, don’t let that stop you from enrolling in this course; we welcome students of all levels.

## Who this course is for

- Aspiring Machine Learning Professionals
- Anyone interested in expanding their skill set with machine learning and Python
- Inquisitive technologists interested in seeing Machine Learning in action
- Those who are already proficient in programming and want to expand their capabilities by learning about machine learning

## Course content

- Introduction
- Welcome to the Python for Data Science & ML bootcamp!
- Python: A Brief Overview
- The Python Installation Procedure
- What Jupyter is?
- Set up Anaconda on Different Operating Systems
- How to integrate Python into Jupyter?
- Handling Directories in Jupyter Notebook
- Input & 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: Built-in Functions
- Functions Part 2: User-defined Functions
- Course Materials

- The Must-Have Python Data Science Libraries
- Completing Library Setup
- Library Importing
- Pandas: A Data Science Library
- NumPy: A Data Science Library
- NumPy vs. Pandas
- Matplotlib Library for Data Science
- Seaborn Library for Data Science

- NumPy Mastery: Everything you need to know about NumPy
- Intro 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
- Element-wise addition
- Element-wise subtraction
- Element-wise multiplication
- Element-wise 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 & 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
- Introduction to Data Cleaning
- Quality of Data
- Examples of Anomalies
- Median-based Anomaly Detection
- Mean-based anomaly detection
- Z-score-based Anomaly Detection
- Interquartile Range for Anomaly Detection
- Dealing with missing values
- Regular Expressions
- Feature Scaling

- 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 Time-Series Analysis: A Primer
- Introduction to Time Series
- Getting stock data using yfinance
- Converting a Dataset into Time Series
- Working with Time Series
- Time Series Data Visualization with Python

- Python for Data Visualization: Library Resources, and Sample Graphs
- Introduction
- Setting Up Matplotlib
- Plotting Line Plots using Matplotlib
- Title, Labels & Legend
- Plotting Histograms
- Plotting Bar Charts
- Plotting Pie Charts
- Plotting Scatter Plots
- Plotting Log Plots
- Plotting Polar Plots
- Handling Dates
- Creating multiple subplots in one figure

- The Basics of Machine Learning
- Why do we need machine learning?
- Machine Learning Use Cases
- Approaches to Machine Learning
- What is Supervised learning?
- What is Unsupervised learning?
- Supervised learning vs Unsupervised learning

- Simple Linear Regression with Python
- Introduction to regression
- How Does Linear Regression Work?
- Line representation
- Implementation in python: Importing libraries & datasets
- Implementation in python: Distribution of the data
- Implementation in python: Creating a linear regression object

- Multiple Linear Regression with Python
- Understanding Multiple linear regression
- Exploring the dataset
- Encoding Categorical Data
- Splitting data into Train and Test Sets
- Training the model on the Training set
- Predicting the Test Set results
- Evaluating the performance of the regression model
- Root Mean Squared Error in Python

- Classification Algorithms: K-Nearest Neighbors
- Introduction to classification
- K-Nearest Neighbors algorithm
- Example of KNN
- K-Nearest Neighbours (KNN) using python
- Importing required libraries
- Importing the dataset
- Splitting data into Train and Test Sets
- Feature Scaling
- Importing the KNN classifier
- Results prediction & Confusion matrix

- Classification Algorithms: Decision Tree
- Introduction to decision trees
- What is Entropy?
- Exploring the dataset
- Decision tree structure
- Importing libraries & datasets
- Encoding Categorical Data
- Splitting data into Train and Test Sets
- Results Prediction & Accuracy

- Classification Algorithms: Logistic regression
- Introduction
- Implementation steps
- Importing libraries & datasets
- Splitting data into Train and Test Sets
- Pre-processing
- Training the model
- Results prediction & Confusion matrix
- Logistic Regression vs Linear Regression

- Clustering
- Introduction to clustering
- Use cases
- K-Means Clustering Algorithm
- Elbow method
- Steps of the Elbow method
- Implementation in python
- Hierarchical clustering
- Density-based clustering
- Implementation of k-means 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
- Content-based Recommender System
- Importing libraries & 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 pre-processing
- Sorting the most-rated movies
- Grabbing the ratings for two movies
- Correlation between the most-rated movies
- Sorting the data by correlation
- Filtering out movies
- Sorting values
- Repeating the process for another movie

- Conclusion
- Conclusion

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