Preview Data Science & Machine Learning

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Module - 1

What is Artificial Inetelligence

What is Machine Learning

What is Deep Learning

What is Data Science

Module - 2 - Numpy Introduction

Introduction to Numpy

How to create an Array in Numpy and different way to create an array

Module - 3 - Indexing and Slicing

Numpy Array Indexing

Numpy Advanced Indexing

Module-4 Different Data Types NumPy and Converting data types in python

NumPy Data Types

Module-4 Array Manipulation

NumPy Array Shape

NumPy Array Reshaping

Module-5 Iteration over an Array

NumPy Array Iterating

Module-6 NumPy Joining the array

NumPy Joining Array

Splitting the Array in NumPy

NumPy Splitting Array

NumPy Array Search

NumPy Searching Arrays

NumPy Array Sort

NumPy Sorting Arrays

Module-7 Numpy-Random

Random Introduction

Normal Distribution "

"2.Binomial Distribution "

"3.poission Distribution "

4.Uniform Distribution

Module-8 Introduction

Introducrion of Pandas

Module-9 Pandas-Series

How to creat a Pandas Series

Module-10 Pnadas Data Frame

How to create a pandas data frame and different ways to create data frame

Data Frame Basic Functionality for Data Science

Iterate over Pandas DataFrame

"1.How to iterate over DataFrame 2.Iterate Using Index Method "

3.Iterate Using loc function

4.Iterate Using Iloc Function

"4.Iterate Using iterrows method "

5.Iterate Using itertuples Method

Module-11 Handling Missing Values

"Theory Session 1.What is Missing Values 2.How to impute the Misisng values"

"Practical Session 3.How to find Missing value with various technique"

4.Handling Missing value with Dropna Function.

5.handle missing value in Numerical Data with fillna function

6.handle and impute missing value on categorical Data with fillna Function

7.Imputing missing value in Both Numerical and Categorical variable at once with Lambda Function

Module-12 Sorting Data

Pandas Sort DataFrame By single Column

"Pandas Sort DataFrame By Multiple Column Sort Data Frame "

Soritng by index in Ascending/ Descending Order

Working with missing data when sorting

Module-13 Guoup By In Pnadas

How to Grouping the Data in Pnadas

How to marge two DataFrame In Pnadas

MatplotLib Tutorials

"1.Introduction of Matplotlib 2.Matplotlib Plotting "

3.Matplotlib Marker

4. Matplotlib LIne

5. Matplotlib set grid line

6.Matplotlib set subplot

7. Matplotlib Scatter Plot

8. Matplotlib Bar Graph

9. Matplotlib Histogram

10. Matplotlib Pie Chart

Statistics

What is Statistics

Types of Data in Data Science

Understanding the difference between a population and a sample

Measure of central Tendancy

What is mean and How to calculate the mean

What is Median How to calculate the median

"The measure of the spread Whatares variance and standard deviation and how to calculate it?"

Calculating and Understanding the covariance

What is distribution

Understand the Normal Distribution

Understand the Standard Normal Distribution

Working with Eastimator and Estimate

Student T Distribution

calculating confidence intervals within a population with an unknown variance

Hypothesis Testing

Test for the mean. Population variance known

What is P-value why it is one of the most useful tools for data science

Machine Learning

What is Machine Learning

What is Supervised Learning

What is unsupervised learning

"Importing the libraries importing the DataSet"

How to Describe the Data

Check Unique Value

How to Filter the Data using loc and Iloc function

Filter and Group the data groupby function

Filter the data using iteration

Filter the data using Crostab method

Filter the data using Pivot table

Find Missing value throgh graph

Count the value through the countplot

Find the skewness in the Data

Find the outliers in the Data

Encoding Categorical Data with one hot encodeing

Encoding Categorical Data with one label encoding

Splitting the dataset into the training set and test

What is Regresion

Simple Linear regression intution

Simple Linear Regression Mathematical equation

R-Square Mathematical Solution

Simple Linear Regression Practical Session

What is Multiple Linear Regression and It's assumption

Multiple Linear Regression Practical Part 1

Polynomial regression intituiion

Polynomial Regression Practical Session

Support Vector Machine Intition

Support Vector Machine Practical Session

Decision Tree intution

Decision Tree Practical Session

Random Forest Intution

Random Forest Practical Session

Logistic Regression Intution

Logistic Regression Practical session

KNN Regression Practical Session

K means Practical session

Preview - Data Science & Machine Learning