Introduction | |||
Welcome to the Python for Data Science & ML bootcamp! | 00:01:00 | ||
Introduction to Python | 00:01:00 | ||
Setting Up Python | 00:02:00 | ||
What is Jupyter? | 00:01:00 | ||
Anaconda Installation Windows Mac and Ubuntu | 00:04:00 | ||
How to implement Python in Jupyter | 00:01:00 | ||
Managing Directories in Jupyter Notebook | 00:03:00 | ||
Input & Output | 00:02:00 | ||
Working with different datatypes | 00:01:00 | ||
Variables | 00:02:00 | ||
Arithmetic Operators | 00:02:00 | ||
Comparison Operators | 00:01:00 | ||
Logical Operators | 00:03:00 | ||
Conditional statements | 00:02:00 | ||
Loops | 00:04:00 | ||
Sequences Part 1: Lists | 00:03:00 | ||
Sequences Part 2: Dictionaries | 00:03:00 | ||
Sequences Part 3: Tuples | 00:01:00 | ||
Functions Part 1: Built-in Functions | 00:01:00 | ||
Functions Part 2: User-defined Functions | 00:03:00 | ||
Course Materials | 00:00:00 | ||
The Must-Have Python Data Science Libraries | |||
Installing Libraries | 00:01:00 | ||
Importing Libraries | 00:01:00 | ||
Pandas Library for Data Science | 00:01:00 | ||
NumPy Library for Data Science | 00:01:00 | ||
Pandas vs NumPy | 00:01:00 | ||
Matplotlib Library for Data Science | 00:01:00 | ||
Seaborn Library for Data Science | 00:01:00 | ||
NumPy Mastery: Everything you need to know about NumPy | |||
Introduction to NumPy arrays | 00:01:00 | ||
Creating NumPy arrays | 00:06:00 | ||
Indexing NumPy arrays | 00:06:00 | ||
Array shape | 00:01:00 | ||
Iterating Over NumPy Arrays | 00:05:00 | ||
Basic NumPy arrays: zeros() | 00:02:00 | ||
Basic NumPy arrays: ones() | 00:01:00 | ||
Basic NumPy arrays: full() | 00:01:00 | ||
Adding a scalar | 00:02:00 | ||
Subtracting a scalar | 00:01:00 | ||
Multiplying by a scalar | 00:01:00 | ||
Dividing by a scalar | 00:01:00 | ||
Raise to a power | 00:01:00 | ||
Transpose | 00:01:00 | ||
Element-wise addition | 00:02:00 | ||
Element-wise subtraction | 00:01:00 | ||
Element-wise multiplication | 00:01:00 | ||
Element-wise division | 00:01:00 | ||
Matrix multiplication | 00:02:00 | ||
Statistics | 00:03:00 | ||
DataFrames and Series in Python's Pandas | |||
What is a Python Pandas DataFrame? | 00:01:00 | ||
What is a Python Pandas Series? | 00:01:00 | ||
DataFrame vs Series | 00:01:00 | ||
Creating a DataFrame using lists | 00:03:00 | ||
Creating a DataFrame using a dictionary | 00:01:00 | ||
Loading CSV data into python | 00:02:00 | ||
Changing the Index Column | 00:01:00 | ||
Inplace | 00:01:00 | ||
Examining the DataFrame: Head & Tail | 00:01:00 | ||
Statistical summary of the DataFrame | 00:01:00 | ||
Slicing rows using bracket operators | 00:01:00 | ||
Indexing columns using bracket operators | 00:01:00 | ||
Boolean list | 00:01:00 | ||
Filtering Rows | 00:01:00 | ||
Filtering rows using AND OR operators | 00:02:00 | ||
Filtering data using loc() | 00:04:00 | ||
Filtering data using iloc() | 00:02:00 | ||
Adding and deleting rows and columns | 00:03:00 | ||
Sorting Values | 00:02:00 | ||
Exporting and saving pandas DataFrames | 00:02:00 | ||
Concatenating DataFrames | 00:01:00 | ||
groupby() | 00:03:00 | ||
Data Cleaning Techniques for Better Data | |||
Introduction to Data Cleaning | 00:01:00 | ||
Quality of Data | 00:01:00 | ||
Examples of Anomalies | 00:01:00 | ||
Median-based Anomaly Detection | 00:03:00 | ||
Mean-based anomaly detection | 00:03:00 | ||
Z-score-based Anomaly Detection | 00:03:00 | ||
Interquartile Range for Anomaly Detection | 00:05:00 | ||
Dealing with missing values | 00:06:00 | ||
Regular Expressions | 00:07:00 | ||
Feature Scaling | 00:03:00 | ||
Exploratory Data Analysis in Python | |||
Introduction (Exploratory Data Analysis in Python) | 00:01:00 | ||
What is Exploratory Data Analysis? | 00:01:00 | ||
Univariate Analysis | 00:02:00 | ||
Univariate Analysis: Continuous Data | 00:06:00 | ||
Univariate Analysis: Categorical Data | 00:02:00 | ||
Bivariate analysis: Continuous & Continuous | 00:05:00 | ||
Bivariate analysis: Categorical & Categorical | 00:03:00 | ||
Bivariate analysis: Continuous & Categorical | 00:02:00 | ||
Detecting Outliers | 00:06:00 | ||
Categorical Variable Transformation | 00:04:00 | ||
Python for Time-Series Analysis: A Primer | |||
Introduction to Time Series | 00:02:00 | ||
Getting stock data using yfinance | 00:03:00 | ||
Converting a Dataset into Time Series | 00:04:00 | ||
Working with Time Series | 00:04:00 | ||
Visualising a Time Series | 00:03:00 | ||
Python for Data Visualisation: Library Resources, and Sample Graphs | |||
Data Visualisation using python | 00:01:00 | ||
Setting Up Matplotlib | 00:01:00 | ||
Plotting Line Plots using Matplotlib | 00:02:00 | ||
Title, Labels & Legend | 00:05:00 | ||
Plotting Histograms | 00:01:00 | ||
Plotting Bar Charts | 00:02:00 | ||
Plotting Pie Charts | 00:03:00 | ||
Plotting Scatter Plots | 00:06:00 | ||
Plotting Log Plots | 00:01:00 | ||
Plotting Polar Plots | 00:02:00 | ||
Handling Dates | 00:01:00 | ||
Creating multiple subplots in one figure | 00:03:00 | ||
The Basics of Machine Learning | |||
What is Machine Learning? | 00:02:00 | ||
Applications of machine learning | 00:02:00 | ||
Machine Learning Methods | 00:01:00 | ||
What is Supervised learning? | 00:01:00 | ||
What is Unsupervised learning? | 00:01:00 | ||
Supervised learning vs Unsupervised learning | 00:04:00 | ||
Simple Linear Regression with Python | |||
Introduction to regression | 00:02:00 | ||
How Does Linear Regression Work? | 00:02:00 | ||
Implementation in python: Importing libraries & datasets | 00:02:00 | ||
Implementation in python: Distribution of the data | 00:02:00 | ||
Implementation in python: Creating a linear regression object | 00:03:00 | ||
Multiple Linear Regression with Python | |||
Understanding Multiple linear regression | 00:02:00 | ||
Exploring the dataset | 00:04:00 | ||
Encoding Categorical Data | 00:05:00 | ||
Splitting data into Train and Test Sets | 00:02:00 | ||
Training the model on the Training set | 00:01:00 | ||
Predicting the Test Set results | 00:03:00 | ||
Evaluating the performance of the regression model | 00:01:00 | ||
Root Mean Squared Error in Python | 00:03:00 | ||
Classification Algorithms: K-Nearest Neighbors | |||
Introduction to classification | 00:01:00 | ||
K-Nearest Neighbours algorithm | 00:01:00 | ||
Example of KNN | 00:01:00 | ||
K-Nearest Neighbours (KNN) using python | 00:01:00 | ||
Importing required libraries | 00:01:00 | ||
Importing the dataset | 00:02:00 | ||
Splitting data into Train and Test Sets | 00:03:00 | ||
Feature Scaling | 00:03:00 | ||
Importing the KNN classifier | 00:02:00 | ||
Results prediction & Confusion matrix | 00:02:00 | ||
Classification Algorithms: Decision Tree | |||
Introduction to decision trees | 00:01:00 | ||
What is Entropy? | 00:01:00 | ||
Exploring the dataset | 00:01:00 | ||
Decision tree structure | 00:01:00 | ||
Importing libraries & datasets | 00:01:00 | ||
Encoding Categorical Data | 00:05:00 | ||
Splitting data into Train and Test Sets | 00:01:00 | ||
Results Prediction & Accuracy | 00:03:00 | ||
Classification Algorithms: Logistic regression | |||
Introduction (Classification Algorithms: Logistic regression) | 00:01:00 | ||
Implementation steps | 00:01:00 | ||
Importing libraries & datasets | 00:02:00 | ||
Splitting data into Train and Test Sets | 00:01:00 | ||
Pre-processing | 00:02:00 | ||
Training the model | 00:01:00 | ||
Results prediction & Confusion matrix | 00:02:00 | ||
Logistic Regression vs Linear Regression | 00:02:00 | ||
Clustering | |||
Introduction to clustering | 00:01:00 | ||
Use cases | 00:01:00 | ||
K-Means Clustering Algorithm | 00:01:00 | ||
Elbow method | 00:02:00 | ||
Steps of the Elbow method | 00:01:00 | ||
Implementation in python | 00:04:00 | ||
Hierarchical clustering | 00:01:00 | ||
Density-based clustering | 00:02:00 | ||
Implementation of k-means clustering in python | 00:01:00 | ||
Importing the dataset | 00:03:00 | ||
Visualising the dataset | 00:02:00 | ||
Defining the classifier | 00:02:00 | ||
3D Visualisation of the clusters | 00:03:00 | ||
3D Visualisation of the predicted values | 00:03:00 | ||
Number of predicted clusters | 00:02:00 | ||
Recommender System | |||
Introduction (Recommender System) | 00:01:00 | ||
Collaborative Filtering in Recommender Systems | 00:01:00 | ||
Content-based Recommender System | 00:01:00 | ||
Importing libraries & datasets | 00:03:00 | ||
Merging datasets into one dataframe | 00:01:00 | ||
Sorting by title and rating | 00:04:00 | ||
Histogram showing number of ratings | 00:01:00 | ||
Frequency distribution | 00:01:00 | ||
Jointplot of the ratings and number of ratings | 00:01:00 | ||
Data pre-processing | 00:02:00 | ||
Sorting the most-rated movies | 00:01:00 | ||
Grabbing the ratings for two movies | 00:01:00 | ||
Correlation between the most-rated movies | 00:02:00 | ||
Sorting the data by correlation | 00:01:00 | ||
Filtering out movies | 00:01:00 | ||
Sorting values | 00:01:00 | ||
Repeating the process for another movie | 00:02:00 | ||
Conclusion | |||
Conclusion | 00:01:00 |
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