Install Anaconda in Linux

Install Anaconda and Python on Windows

Course Introduction

Machine Learning Introduction

Jupyter Notebook Introduction and Keyboard Shortcuts

Boolean Variables and Evaluation

Set

Variable Casting

String Formatting and Modification

List in Python

For Loops

While Loops

Data Types in Python

Tuple in Python

String Slicing in Python

Arithmatic Operations in Python

Dictionary

Strings Operation in Python

Conditional Statements - If Else

Functions

Working with Date and Time

File Handling Read and Write

np.nan and np.inf

Shape(), Reshape(), Ravel(), Flatten()

Statistical Operations

Where

Numpy Introduction - Create Numpy Array

Array Indexing and Slicing

Concatenation and Sorting

arange(), linspace(), range(), random(), zeros(), and ones()

Numpy Data Types

Numpy Array Read and Write

Pandas Series Introduction Part 1

Arithmetic Operations

Pandas Series Read From File

Pandas Series Introduction Part 2

Pandas DataFrame Creation from Scratch

apply() for Pandas Series

Merging, Joining, and Concatenation Part 1

Columns Manipulation Part 1

Apply Pythons Built in Functions to Series

Rename, Delete Index and Columns

Columns Manipulation Part 2

NULL Values Handling

Replace Cell Values

Concatenation

Read Stock Data from YAHOO Finance

Retrive Rows by Index Label

Read Files as DataFrame

Handling Unique and Duplicated Values

DataFrame Data Filtering Part 2

DataFrame Data Filtering Part 1

Lambda Apply

Groupby Multiple Columns

Merge and Join

Pandas Groupby

IMDB Movie Revenue Line Plot Part 2

IMDB Movie Revenue Line Plot Part 1

Line Plot Rank vs Runtime Votes Metascore

Matplotlib Introduction

Line Styling and Putting Labels

Matplotlib Line Plot Part 1

Scatter, Bar, and Histogram Plot Part 1

xlim and ylim, legend, grid, xticks, yticks

Subplot Part 1

Creating a Zoomed Sub-Figure of a Figure

Scatter, Bar, and Histogram Plot Part 2

Pie Chart and Figure Save

Subplot Part 2

Subplots

Hue, Style and Size Part1

Line Plot Part 1

Box Plot

Point Plot

Scatter Plot

Introduction

Hue, Style and Size Part2

Joint Plot

Boxen Plot

Subplots

cat plot

Violin Plot

Bar Plot

Regression Plot

sns.lineplot() and sns.scatterplot()

Pair Plot

Controlling Ploted Figure Aesthetics

Line Plot Part 3

Line Plot Part 2

IRIS Dataset Introduction

Load IRIS Dataset

Box Plot

Stacked Bar Plot

Hexbin Plot

Bar and Barh Plot

Line Plot

Secondary Axis

Area and Scatter Plot

Histogram

Scatter Matrix and Subplots

Pie Chart

Introduction to Plotly and Cufflinks

Scatter Plot

Box and Area Plot

D Plot

Plotly Line Plot

Stacked Bar Plot

Hist Plot, Bubble Plot and Heatmap

Linear Regression Introduction

Regression Examples

Assessing the performance of the model

Load Boston Housing Dataset

Python Package Upgrade and Import

What is sklearn and train-test-split

Machine Learning Model Interpretability- Prediction Error Plot

Exploratory Data Analysis- Hist Plot

Types of Linear Regression

Plotting Learning Curves Part 1

Machine Learning Model Interpretability- Residuals Plot

Bias-Variance tradeoff

Dataset Analysis

Exploratory Data Analysis- Heatmap

Train Test Split and Model Training

Plot True House Price vs Predicted Price

Plotting Learning Curves Part 2

How to Evaluate the Regression Model Performance

Exploratory Data Analysis- Pair Plot

Sigmoid Function

Logistic Regression Introduction

Decision Boundary

EDA - Heatmap and Density Plot

Titanic Dataset Introduction

Accuracy, F1-Score, P, R, AUC ROC Curve Part 1

Missing Age Imputation Part 1

Train Test Split

Dataset Loading

One-Hot Encoding

Accuracy, F1-Score, P, R, AUC ROC Curve Part 2

ROC Curve and AUC Part 2

Imputation of Missing Embark Town

Data Types Correction and Mapping

Accuracy, F1-Score, P, R, AUC ROC Curve Part 3

Missing Age Imputation Part 2

Model Building Training and Evaluation

ROC Curve and AUC Part 3

ROC Curve and AUC Part 1

Feature Selection - Recursive Feature Elimination

SVM Introduction

SVM Kernels

Dataset Loading

Train Test Split

Polynomial, Sigmoid, RBF Kernels in SVM

Breast Cancer Dataset Introduction

Data Standardization

Cancer Data Visualization Part 1

Linear SVM Model on Scaled Feature

Linear SVM Model Building and Training

Cancer Data Visualization Part 2

Cross Validation Regularization and Hyperparameter Optimization Introduction

ML Model Training Process

Linear Regression and SVM Model Training

Random Grid Search Hyperparameter Tuning

Types of Cross Validation

Train Test Split

Breast Cancer Dataset Loading

Regularization Introduction

Data Visualization

K-Fold and LeaveOneOut Cross Validation

Manual Hyperparameter Adjustment

Grid Search Hypyerparameter Tuning

Pros and Cons of KNN

KNN Model Building and Training

KNN Introduction

Train Test Split and Standardization

How KNN Works

Wine Dataset Laoding

Hyperparameter Tuning

Train Test Split

Model Training and Evaluation

Decision Tree Introduction

How Decision Tree Works

Hyperparameter Optimization

Tree Visualization

What is Attribute Selection Measures - ASM

Dataset Visualization

Decision Tree Regression

Diabetes Dataset Loading

Random Forest Regression Model Building

Ensemble Learning Bagging and Boosting Introduction

Train Test Split and One-Hot Encoding

Dataset Introduction

Random Forest Introduction

Random Forest Classifier Training and Evaluation

Data Loading for Random Forest Regression

Train Test Split

AdaBoost Hyperparameter Tuning

XGBoost Introduction

AdaBoost Model Training

CatBoost Model Training

Boosting Algorithms Introduction

XGBoost Model Training and Hyperparameter Tuning

Data Visualization Part 1

Heart-Disease Dataset Understanding

CatBoost Hyperparameter Optimization

K-Means Clustering with Scikit-Learn

Introduction to K-Means

Introduction to Unsupervised Learning

Customers Data Loading

Application of Unsupervised Learning

K-Means Clustering for Age and Spending Score

How to Choose Best Number of Clusters

K-Means Clustering Data Preparation

D Clustering Part 1

Selecting Optimum Number of Clusters

Clustering for Annual Income vs Spending Score

D Clustering Part 2

Data Visualization

Clusters Visualization

Decision Boundary Visualization

Putting Everything Together

Generate Dataset

Spectral Clustering Coding

DBSCAN Introduction

DBSCAN Clustering

Spectral Clustering

PCA Applications

PCA Introduction

PCA Compression Analysis

Data Reconstruction with 95% Information

MNIST Dataset Loading and Understanding

How PCA is Done

Choosing Right Number of the Principle Components

Classification Comparison with and without PCA

PCA Coding

Data Reconstruction

Hierarchical Clustering Introduction

Important Terms in Hierarchical Clustering

Hierarchical Clustering Coding

Stock Market Data Loading

Shallow vs Deep Neural Networks

What is Neuron

Customer Churn Dataset Loading

Activation Function

How to Get Input Shape and Class Weights

Model Save and Load

Model Evaluation

Multi-Layer Perceptron

Data Preprocessing

Import Neural Networks APIs

Data Visualization Part 1

Optimizers in Deep Learning

Model Summary Explanation

Neural Network Model Building

Install TensorfFlow in Windows

Install TensorFlow in Linux

Steps to Build Neural Network

Model Training

Prediction on Real-Life Data

What is Back Propagation

Data Visualization Part 2

Train Test Split

Common Challenges in NLP

Load Spam Dataset

Introduction to NLP

Term Frequency - Inverse Document Frequency (TF-IDF)

Bag of Words - The Simples Word Embedding Technique

What are Key NLP Techniques

Feature Engineering

TF-IDF Vectorization

Model Load and Store

Model Evaluation and Prediction on Real Data

Pair Plot

Text Preprocessing

Overview of NLP Tools