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.
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
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
