Data Science : Complete Data Science & Machine Learning 

Course Introduction

Part 1: Essential Python Programming

Install Anaconda, Spyder

Keyboard Shortcut - Must view for beginners

Hands On - Hello Python and Know the environment

Hands On - Variable Types and Operators

Hands On - Decision Making - If-Else

Python Loops explained

Hands On - While Loops

Hands On - For Loops

Python Lists Explained

Hands On - Lists Basic Operations

Hands On - Lists Operations Part 2

Multidimensional Lists Explained

Hands On - Slicing Multidimensional lists

Hands On - Python Tuples

Python Dictionary Explained

Hands On - Access the Dictionary Data

Hands On - Dictionary Methods and functions

File processing - Open and Read files

File Processing - Process Data and Write to Files

File Processing - Process Data using Loops

Project 1 - Calculate the average temperature per city

Solution - Project 1 calculate the average temperature per city

Essential Python Programming

What you will learn in this Part?

Algebraic Equations

Exponents and Logs

Polynomial Equations

Factoring

Quadratic Equations

Functions

Algebra Foundations

Calculus Foundation

Rate of Change and Limits

Differentiation and Derivatives

Derivative Rules and Operations

Double Derivatives and finding Maxima

Double Derivatives example

Partial Derivatives and Gradient Descent

Integration and Area Under the Curve

Vector Arithmetic

Matrix Foundation

Matrix Arithmetic

Identity, Inverse, Determinant and Transpose Matrix

Matrix Transformation

Change of Basis and Axis using Matrix Transformation

Eigenvalues and Eigenvectors

Linear Algebra

Understanding probability in simple terms

Probability Terms

Conditional Probability

Random Processes and Random Variables

Probability Foundation

What is Data Science and Machine Learning?

Need for Data Science and Machine Learning

Types of Analytics

Decoding Data Science and Machine Learning

Data Science Project Lifecycle Part 1

Data Science Project Lifecycle Part 2

Data Science Project Lifecycle Part 3

Data Science Project Lifecycle Part 4

What does a Data Scientist do and the skills required?

Data Science Basics

Descriptive Statistics

What is Data? Understanding the Data and its elements.

Measure of Central Tendency using Mean, Median, mode

Measure of Dispersion using Standard Deviation and variance

Hands on - Get Statistical Summary

Measure of Dispersion using Percentile, Range and IQR

Data Visualization

Importance of Data Visualization

Data Visualization - Frequency Table, Histogram and Bar Chart

Understanding Boxplot for Numerical Data

What is a Plot?

Hands On - Create Line Plots

Hands On - Understand Plot Figure Menu

Hands On - Create your first Bar Chart

Hands On - Create Histogram of Data

Hands On - Plotting Boxplot

Data Visualization for Categorical Data

Hands On - Pie Charts Part 1

Hands On - Pie Charts Part 2

Hands On - Scatter Plots

Hands On - MatplotLib Figures for creating multiple plots

Hands On - Subplots for plotting multiple plots in one figure

Hands On - Customization of Plot elements Part 1

Hands On - Customization of Plot elements Part 2

Hands On - Customization of Plot elements Part 3

Hands On - Customization of Plot elements Part 4

Inferential Statistics, Distributions and Hypothesis

Understand Population Vs Samples

What is a Sample Bias?

What is Correlation and Causality?

What is Covariance and Covariance Matrix?

Probability Density Function and Distributions

Normal Distributions

Standard Normal Distributions

Sampling Distributions

Central Limit Theorem

Confidence Interval - Part 1

Confidence Interval - Part 2

What is Hypothesis and Null Vs Alternate Hypothesis?

What is Statistical Significance

Hypothesis Testing Examples

Hands On - Understand the data you are dealing with

Hands On - Handling Missing Values

Label-Encoding for Categorical Data

Hands On Label Encoding

Hot-Encoding for Categorical Data Explained

Hands On - Hot-Encoding for Categorical Data

Data normalization - Understand the reasons.

Hands On - Data Normalization using Standard Scaler

Hands On - Data Normalization using minmax

Train and Test Data Split explained

Hands On - Train and Test Data Split

-- Part 5 Regression

What is Simple Linear Regression

Ordinary Least Square and Regression Errors

Project - Data Processing

Project - Train and Test Model

Test the model and Predict Y Values

Project - R-Squared and its Importance

Project - Score and Get coefficients

Project - Calculate RMSE (Root Mean Squared Error)

Project - Plot the predictions

Multiple Linear Regression

Understanding the Multiple Linear Regression

Project - Multiple Linear Regression Predictions

Issues to deal with for Multiple Linear Regression

Degrees of Freedom

Adjusted R-Squared

Assumptions of Multiple Linear Regression

Linearity and Multicollinearity Assumption

Assumption of Autocorrelation

Hands on - Plot Autocorrelation

Hands on - Create shifted or TimeLag Data

Endogeneity Assumption

Normality of Residuals

Assumption of Homoscadasticity

Dummy Variable trap

Project - Kaggle Bike Demand Predictions

Let's understand the problem

Steps required to solve the problem

Read and Prepare Data

Basic Analysis of Data

Data Visualization of the Continuous Variables

Data Visualization of the Categorical Variables

Summarize Data Visualization Findings

Preview

Test the Multicollinearity Assumption

Test Auto-correlation in Demand

Solving the problem of Normality

Solving the problem of Autocorrelation

Create Dummy Variables

Train-Test Split for the Time-Series Data

Create the Model and measure RMSE

Calculate and measure RMSLE for Kaggle

What is Logistic Regression?

Project - Predict Loan Approval Problem Understanding

Project - Predict Loan Approval

Project - Predict Loan Approval - Build Logistic Regressor

Project - Predict Loan Approval - Confusion Martix

Create and Analyse Confusion Matrix

Support Vector Machines (SVM)

Common Sensical Intuition of SVM

Mathematical Intuition of SVM

Hands on - Simple Implementation of SVM

SVM Kernel Functions

SVM Kernel Function Types

Project - IRIS Classification Problem

Project - Data Processing

Project - Train and create Model

Project - Multiple Model Creation and comparison

Decision Trees lectures

Intuition Behind Decision Trees

Project - Adult Income Prediction Problem Understanding

Project - Data Processing

Project - Split data and Import Classifier

Project - Decision Trees - Parameters Part

Project - Run and Evaluate Model

Random Forest lectures

Ensemble Learning and Random Forests

Bagging and Boosting

Hands on - Implement Random Forest

Evaluate Classification Models lectures

Need for Evaluation and Accuracy Paradox

Classification Evaluation Measures

Hands on - Evaluation Metrics for Loan Prediction projects

What is Threshold and Adjusting Thresholds

Hands on - Adjusting Thresholds

Hands On - AUC ROC Curve using Python

Drawing the AUC ROC Curve

Univariate Feature Selection

Feature Selection Importance

What is Univariate Feature Selection?

F-Test for Regression and Classification

Hands on F-test - Problem Statement

Hands On F-test - Regression without feature selection

Hands on F-test - Print and analyse Pvalues

Hands on F-test - Compare Results with and without Feature Selection

Chi-Squared Intuition

Scikitlearn - What are Feature Selection Transforms

Hands on - SelectKBest

Hands on - SelectPercentile

Hands on - Generic Univariate

Recursive Feature Elimination

What is Recursive Feature Elimination (RFE)?

Project - Bank Telemarketing Predictions Problem Understanding

Project - Build Prediction model without RFE

Project - Configure RFE and Compare results

Project - Get Feature Importance Score

-- Part 8 Dimensionality Reduction --

Why to reduce dimensions and Importance of PCA?

Mathematical Intuition of PCA and Steps to calculate PCA

Project - Model Implementation without PCA

Project - Convert the Dimensions to PCA

Project - Compare results after PCA Implementation

Regularization Introduction.

What is Bias Variance Trade-off?

Ridge Regression or L Penalty

Hands on - Implement Ridge Regression

Hands on - Plot Ridge Regression Line

Hands On - Effect of Lambda/Alpha

Lasso Regression or L Penalty - Hands on

L and L for Multicollinearity and Feature Selection

Elasticnet Regularization

Model Selection Introduction

Cross Validation for Model Selection

What is Cross Validation?

How Cross Validation Works

Hands On - Prepare for Cross Validation

Hands On - Parameter and implementation of Cross Validation

Hands On - Understand the results of Cross Validation

Hands On - Analyse the Result

Hyperparameter Tuning for Model Selection

What is Hyperparameter Tuning?

Grid Search and Randomized Search Approach

GridSearchCV Parameters Explained

Create GirdSearchCV Object

Fit data to GridSearchCV

Understand GridSearchCV Results

GridSearchCV using Logistic Regression

GridSearchCV using Support Vector

Select Best Model

Randomized Search

Model Selection Summary

What is Neuron and Artificial Neural Network?

How Artificial Neural Network works?

What is Keras and Tensorflow?

What is a Tensor in Tensorflow?

Installing Keras, backend and Tensorflow

Keras Model Building and Steps

Layers - Overview and Parameters

Activation Functions

Layers - Softmax Activation Function

What is a Loss Function?

Cross Entropy Loss Functions

Optimization - What is it?

Optimization - Gradient Descent

Optimization - Stochastic Gradient Descent

Optimization - SGD with Momentum

Optimization - SGD with Exponential Moving Average

Optimization - Adagrad and RMSProp for Learning rate decay

Optimization - Adam

Initializers - Vanishing and Exploding Gradient Problem

Layers - Initializers explained

Project - Understand the Problem

Updated Program for the Tensorflow .

Project - Read and process the data

Project - Define the Keras Neural Network Model

Project - Evaluate the result

What is Clustering?

How the clusters are formed?

Project - Problem Understanding

Project - Get, Visualize and Normalize the data

Project - Import KMeans and Understand Parameters

Project - Understanding KMeans++ Initialization Method

Project - Create Clusters

Project -Visualize and create different number of clusters

Understand Elbow Method to Decide number of Cluster

Project - Implement Elbow Method

How to use clustering for business?

Way Forward.