Python & Machine Learning Mastery for Data Science Warriors
-
1
-
Introduction: About the Tutor and AI Sciences
-
Introduction: Introduction To Instructor
-
Introduction: Focus of the Course-Part 1
-
Introduction: Focus of the Course- Part 2
-
Basics of Programming: Understanding the Algorithm
-
Basics of Programming: FlowCharts and Pseudocodes
-
Basics of Programming: Example of Algorithms- Making Tea Problem
-
Basics of Programming: Example of Algorithms-Searching Minimun
-
Basics of Programming: Example of Algorithms-Searching Minimun Quiz
-
Basics of Programming: Example of Algorithms-Searching Minimun Solution
-
Basics of Programming: Example of Algorithms-Sorting Problem
-
Basics of Programming: Sorting Problem in Python
-
Why Python and Jupyter Notebook: Why Python
-
Why Python and Jupyter Notebook: Why Jupyter Notebooks
-
Installation of Anaconda and IPython Shell: Installing Python and Jupyter AnaconDA
-
Installation of Anaconda and IPython Shell: Your First Python Code- Hello World
-
Installation of Anaconda and IPython Shell: Coding in IPython Shell
-
Variable and Operator: Variables
-
Variable and Operator: Operators
-
Variable and Operator: Variable Name Quiz
-
Variable and Operator: Bool Data Type in Python
-
Variable and Operator: Comparison in Python
-
Variable and Operator: Combining Comparisons in Python
-
Variable and Operator: Combining Comparisons Quiz
-
Python Useful function: Python Function- Round
-
Python Useful function: Python Function- Round Quiz
-
Python Useful function: Python Function- Round Solution
-
Python Useful function: Python Function- Divmod
-
Python Useful function: Python Function- Is instance and PowFunctions
-
Python Useful function: Python Function- Input
-
Control Flow in Python: If Python Condition
-
Control Flow in Python: if Elif Else Python Conditions
-
Control Flow in Python: if Elif Else Python Conditions Quiz
-
Control Flow in Python: if Elif Else Python Conditions Solution
-
Control Flow in Python: More on if Elif Else Python Conditions
-
Control Flow in Python: More on if Elif Else Python Conditions Quiz
-
Control Flow in Python: More on if Elif Else Python Conditions Solution
-
Control Flow in Python: Indentations
-
Control Flow in Python: Indentations Quiz
-
Control Flow in Python: Indentations Solution
-
Control Flow in Python: Comments and Problem Solving Practice With If
-
Control Flow in Python: While Loop
-
Control Flow in Python: While Loop break Continue
-
Control Flow in Python: While Loop break Continue Quiz
-
Control Flow in Python: While Loop break Continue Solution
-
Control Flow in Python: For Loop
-
Control Flow in Python: For Loop Quiz
-
Control Flow in Python: For Loop Solution
-
Control Flow in Python: Else In For Loop
-
Control Flow in Python: Loops Practice-Sorting Problem
-
Function and Module in Python: Functions in Python
-
Function and Module in Python: DocString
-
Function and Module in Python: Input Arguments
-
Function and Module in Python: Multiple Input Arguments
-
Function and Module in Python: Multiple Input Arguments Quiz
-
Function and Module in Python: Multiple Input Arguments Solution
-
Function and Module in Python: Ordering Multiple Input Arguments
-
Function and Module in Python: Output Arguments and Return Statement
-
Function and Module in Python: Function Practice-Output Arguments and Return Statement
-
Function and Module in Python: Variable Number of Input Arguments
-
Function and Module in Python: Variable Number of Input Arguments Quiz
-
Function and Module in Python: Variable Number of Input Arguments Solution
-
Function and Module in Python: Variable Number of Input Arguments as Dictionary
-
Function and Module in Python: Variable Number of Input Arguments as Dictionary Quiz
-
Function and Module in Python: Variable Number of Input Arguments as Dictionary Solution
-
Function and Module in Python: Default Values in Python
-
Function and Module in Python: Modules in Python
-
Function and Module in Python: Making Modules in Python
-
Function and Module in Python: Function Practice-Sorting List in Python
-
String in Python: Strings
-
String in Python: Multi Line Strings
-
String in Python: Indexing Strings
-
String in Python: Indexing Strings Quiz
-
String in Python: Indexing Strings Solution
-
String in Python: String Methods
-
String in Python: String Methods Quiz
-
String in Python: String Methods Solution
-
String in Python: String Escape Sequences
-
String in Python: String Escape Sequences Quiz
-
String in Python: String Escape Sequences Solution
-
Data Structure: Introduction to Data Structure
-
Data Structure: Defining and Indexing
-
Data Structure: Insertion and Deletion
-
Data Structure: Insertion and Deletion Quiz
-
Data Structure: Insertion and Deletion Solution
-
Data Structure: Python Practice-Insertion and Deletion
-
Data Structure: Python Practice-Insertion and Deletion Quiz
-
Data Structure: Python Practice-Insertion and Deletion Solution
-
Data Structure: Deep Copy or Reference Slicing
-
Data Structure: Deep Copy or Reference Slicing Quiz
-
Data Structure: Deep Copy or Reference Slicing Solution
-
Data Structure: Exploring Methods Using TAB Completion
-
Data Structure: Data Structure Abstract Ways
-
Data Structure: Data Structure Practice
-
Data Structure: Data Structure Practice Quiz
-
Data Structure: Data Structure Practice Solution
-
-
3
-
Introduction to the Course: Introduction to the Course
-
Introduction to the Course: Introduction To Instructor
-
Introduction to the Course: Focus of the Course
-
Introduction to the Course: Python Practical of the Course
-
Why Machine Learning: Machine Learning Applications-Part 1
-
Why Machine Learning: Machine Learning Applications-Part 2
-
Why Machine Learning: Why Machine Learning is Trending Now
-
Process of Learning from Data: Supervised Learning
-
Process of Learning from Data: UnSupervised Learning and Reinforcement Learning
-
Machine Learning Methods: Features
-
Machine Learning Methods: Features Practice with Python
-
Machine Learning Methods: Regression
-
Machine Learning Methods: Regression Practice with Python
-
Machine Learning Methods: Classsification
-
Machine Learning Methods: Classification Practice with Python
-
Machine Learning Methods: Clustering
-
Machine Learning Methods: Clustering Practice with Python
-
Data Preparation and Preprocessing: Handling Image Data
-
Data Preparation and Preprocessing: Handling Video and Audio Data
-
Data Preparation and Preprocessing: Handling Text Data
-
Data Preparation and Preprocessing: One Hot Encoding
-
Data Preparation and Preprocessing: Data Standardization
-
Machine Learning Models and Optimization: Machine Learning Model 1
-
Machine Learning Models and Optimization: Machine Learning Model 2
-
Machine Learning Models and Optimization: Machine Learning Model 3
-
Machine Learning Models and Optimization: Training Process, Error, Cost and Loss
-
Machine Learning Models and Optimization: Optimization
-
Building Machine Learning Model from Scratch: Linear Regression from Scratch- Part 1
-
Building Machine Learning Model from Scratch: Linear Regression from Scratch- Part 2
-
Building Machine Learning Model from Scratch: Minimun-to-mean Distance Classifier from Scratch- Part 1
-
Building Machine Learning Model from Scratch: Minimun-to-mean Distance Classifier from Scratch- Part 2
-
Building Machine Learning Model from Scratch: K-means Clustering from Scratch- Part 1
-
Building Machine Learning Model from Scratch: K-means Clustering from Scratch- Part 2
-
Overfitting, Underfitting and Generalization: Overfitting Introduction
-
Overfitting, Underfitting and Generalization: Overfitting example on Python
-
Overfitting, Underfitting and Generalization: Regularization
-
Overfitting, Underfitting and Generalization: Generalization
-
Overfitting, Underfitting and Generalization: Data Snooping and the Test Set
-
Overfitting, Underfitting and Generalization: Cross-validation
-
Machine Learning Model Performance Metrics: The Accuracy
-
Machine Learning Model Performance Metrics: The Confusion Matrix
-
4
-
Support Vector Machine: Introduction SVM
-
Support Vector Machine: Linear Discriminants
-
Support Vector Machine: Linear Discriminants higher spaces
-
Support Vector Machine: Linear Discriminants Decision Boundary
-
Support Vector Machine: Generalized Linear Model
-
Support Vector Machine: Feature Transformation
-
Support Vector Machine: Max Margin Linear Discriminant
-
Support Vector Machine: Hard Margin Vs Soft Margin
-
Support Vector Machine: Confidence
-
Support Vector Machine: Multiclass Extension
-
Support Vector Machine: SVM Vs Logistic Regression Sparsity
-
Support Vector Machine: SVM Optimization
-
Support Vector Machine: SVM Langrangian Dual
-
Support Vector Machine: Kernels
-
Support Vector Machine: Python Packages & Titanic DataSet
-
Support Vector Machine: Using Numpy, Pandas and Matplotlib (Part 1)
-
Support Vector Machine: Using Numpy, Pandas and Matplotlib (Part 2)
-
Support Vector Machine: Using Numpy, Pandas and Matplotlib (Part 3)
-
Support Vector Machine: Using Numpy, Pandas and Matplotlib (Part 4)
-
Support Vector Machine: Using Numpy, Pandas and Matplotlib (Part 5)
-
Support Vector Machine: Using Numpy, Pandas and Matplotlib (Part 6)
-
Support Vector Machine: DataSet Preprocessing
-
Support Vector Machine: SVM with Sklearn
-
Support Vector Machine: SVM without Sklearn (Part 1)
-
Support Vector Machine: SVM without Sklearn (Part 2)
-
Optional SVM Section: Optional SVM Optimization (Part 1)
-
Optional SVM Section: Optional SVM Optimization (Part 2)
-
Optional SVM Section: Optional SVM Optimization (Part 3)
-
Optional SVM Section: Optional SVM Optimization (Part 4)
-
Optional SVM Section: Optional SVM Optimization (Part 5)
-
Optional SVM Section: Optional SVM Optimization (Part 6)
-
5
-
Random Forest Step-by-step: Introduction and Motivation
-
Random Forest Step-by-step: How Decision Trees and Random Forest Work
-
Random Forest Step-by-step: Pros and Cons of Random Forest
-
Random Forest Step-by-step: Introduction to the final Project
-
Random Forest Step-by-step: Using NumPy for Random Forest
-
Random Forest Step-by-step: Using Pandas for Random Forest (1)
-
Random Forest Step-by-step: Using Pandas for Random Forest (2)
-
Random Forest Step-by-step: Reading and Manipulating Dataset
-
Random Forest Step-by-step: Using Matplotlib for Data Visualization (1)
-
Random Forest Step-by-step: Using Matplotlib for Data Visualization (2)
-
Random Forest Step-by-step: Dealing with Missing Values
-
Random Forest Step-by-step: Outliers Removal
-
Random Forest Step-by-step: Categorical to Numeric Conversion
-
Random Forest Step-by-step: Quick Implementation of Random Forest Model
-
Random Forest Step-by-step: Feature Importance
-
Random Forest Step-by-step: Recursion
-
Random Forest Step-by-step: Structure
-
Random Forest Step-by-step: Importing Data, Helper Functions
-
Random Forest Step-by-step: Question and Partition
-
Random Forest Step-by-step: Impurity
-
Random Forest Step-by-step: Information Gain
-
Random Forest Step-by-step: Best Slip
-
Random Forest Step-by-step: Leaf and Decision Node
-
Random Forest Step-by-step: How to Build Tree
-
Random Forest Step-by-step: Classify
-
Random Forest Step-by-step: Accuracy and Error
-
6
-
Logistic Regression Step-by-Step: Introduction to Logistic Regression and Motivation
-
Logistic Regression Step-by-Step: Pros and Cons
-
Logistic Regression Step-by-Step: Introduction to the final Project
-
Logistic Regression Step-by-Step: Numpy
-
Logistic Regression Step-by-Step: Pandas (1)
-
Logistic Regression Step-by-Step: Pandas (2)
-
Logistic Regression Step-by-Step: Reading and Manipulating Dataset
-
Logistic Regression Step-by-Step: Matplotlib (1)
-
Logistic Regression Step-by-Step: Matplotlib (2)
-
Logistic Regression Step-by-Step: Dealing with Missing Values
-
Logistic Regression Step-by-Step: Outliers Removal
-
Logistic Regression Step-by-Step: Categorical to Numeric
-
Logistic Regression Step-by-Step: ScikitLearn - Quick Implementation of Logistic Regression
-
Logistic Regression Step-by-Step: Sigmoid Function
-
Logistic Regression Step-by-Step: Decision Boundary
-
Logistic Regression Step-by-Step: Cost Function
-
Logistic Regression Step-by-Step: Gradient Decent
-
Logistic Regression Step-by-Step: Logistic Regression from Scratch (1)
-
Logistic Regression Step-by-Step: Logistic Regression from Scratch (2)
-
Logistic Regression Step-by-Step: Logistic Regression from Scratch (3)
-
Logistic Regression Step-by-Step: Logistic Regression from Scratch (4)
-
Logistic Regression Step-by-Step: Logistic Regression from Scratch (5)
-
Logistic Regression Step-by-Step: Logistic Regression from Scratch (6)
-
Logistic Regression Step-by-Step: Binary to Multiclass
-
7
-
Introduction: Introduction To Instructor
-
Introduction: Introduction to Course
-
House Price Prediction: Module Introduction
-
House Price Prediction: Installing-Importing Libraries
-
House Price Prediction: Importing & Visualizing Dataset
-
House Price Prediction: Dataset
-
House Price Prediction: Feature Label Split
-
House Price Prediction: Train Test Split
-
House Price Prediction: ML Model
-
House Price Prediction: Liner Regression
-
House Price Prediction: Model Evaluation
-
House Price Prediction: Making Predictions
-
House Price Prediction: Quiz
-
Email Filtration: Module Introduction
-
Email Filtration: Data Import Split
-
Email Filtration: Removing Stopwords
-
Email Filtration: Making Word Cloud
-
Email Filtration: Count Vectorizer Explained
-
Email Filtration: Vectorizing Text Feature
-
Email Filtration: Model Implementation & Evaluation
-
Email Filtration: NB Details and Types
-
Email Filtration: Making Predictions
-
Email Filtration: Quiz
-
Car Price Predication: Module Introduction
-
Car Price Predication: Getting Started with Colab
-
Car Price Predication: Loading Data in Colab
-
Car Price Predication: Data Visualization
-
Car Price Predication: One Hot Encoding
-
Car Price Predication: Data Standardization
-
Car Price Predication: Deep Learning Explained
-
Car Price Predication: Model Architecture
-
Car Price Predication: Model Training
-
Car Price Predication: Model Evaluation
-
Car Price Predication: Making Predictions
-
Car Price Predication: Quiz
-
Customer Segmentation Kmean: Module Introduction
-
Customer Segmentation Kmean: Imports and Data Intro
-
Customer Segmentation Kmean: Data Visualization & Analysis
-
Customer Segmentation Kmean: K Means
-
Customer Segmentation Kmean: Model Implementation
-
Customer Segmentation Kmean: Ploting the Centroids
-
Customer Segmentation Kmean: Finding the Optimal Value
-
Customer Segmentation Kmean: Cluster Map
-
Customer Segmentation Kmean: Quiz
-
Employee Retention Classification for HR: Module Introduction
-
Employee Retention Classification for HR: Libraries & Dataset
-
Employee Retention Classification for HR: Data Analysis
-
Employee Retention Classification for HR: Model Training & Evaluation
-
Employee Retention Classification for HR: Heatmap of Predictions