Artificial Intelligence
New
Level1(Understanding Of AI)  
What is AI  Sub Topic 
Introduction  What is AI? 
What does AI do?  
Where did AI come from?  
Why People Worried about AI  
Types of AI  
What to Expect from AI and its day to day
uses 

Steps to Built a good AI Model  Identify the problem. 
Get and Prepare the data.  
Choose the algorithms.  
Train the algorithms.  
Choose a programming language.  
Run on a selected platform.  
Understanding the Data  Sub Topic 
What is data  Definition of Data 
Understanding the data around us  
Type of Data  Data Definition 
Understanding Structured Data  What Is Structured data 
Sources in everyday life  
Concept required to understand Structure data  
Understanding Un Structured Data  What Is Un Structured data 
Sources in everyday life  
Concept required to understand Un Structure data  
Power Of BI  Operating On BI Tool for Data Interpretation 
Level2  
Preparing the Data  Subtopic 
Extracting the data  Using SQL 
Clean the data  Using SQL 
Data Spliting  Using SQL 
Getting Data Ready For AI systems  
Python Topics  Subtopic 
Introduction To Python  Python Installation 
Using Jupiter  
Using Anaconda  
Values and Variables  Integer Values 
Variables and Assignment  
Identifiers  
Floatingpoint Types  
Control Codes within Strings  
User Input  
The eval Function  
Controlling the print Function 
Expressions and Arithmetic  Expressions 
Operator Precedence and Associativity  
Comments  
Errors  
Syntax Errors  
RunTime Errors  
Logic Errors  
Arithmetic Examples  
More Arithmetic Operators  
Algorithms  
Conditional Execution  Boolean Expressions 
Boolean Expressions 2.0  
The Simple if Statement  
The ifelse Statement  
Compound Boolean Expressions  
Nested Conditionals  
Multiway Decision Statements  
Conditional Expressions  
Errors in Conditional Statements  
Iteration  The while statement 
Definite Loops vs Indefinite Loops  
The for statement  
Nested Loops  
Abnormal Loop Termination  
The break statement  
The continue statement  
Infinite Loops  
Iteration Examples  
Computing Square root  
Drawing a Tree  
Printing Prime Number  
Insisting on the Proper Input  
Numpy Library  Introduction to NumPy 
Understanding Data Types in Python  
Fixed Type Arrays in Python  
Creating Arrays from Scratch  
The Basics of NumPy Arrays  
Array Slicing – Accessing Subarrays  
Reshaping of Arrays  
Splitting of Arrays  
Splitting of Arrays – 2  
Exploring NumPy’s UFuncs  
Exponents and Logarithms  
Advanced UFunc Functions 
Aggregations  
Computation on Arrays – Broadcasting  
Broadcasting Example  
Broadcasting in Practice  
Comparisons, Masks and Boolean Logic  
Boolean Operators  
Boolean Arrays as Masks  
Fancy Indexing  
Combined Indexing  
Modifying values with Fancy Indexing  
Sorting Arrays  
Fast Sorting in NumPy  
Example: kNearest Neighbors  
Structured Data – NumPy’s Structured Arrays  
Data Manipulation with Pandas  Introduction 
Installing and Using Pandas  
Introducing Pandas Objects  
Constructing Series Objects  
The Pandas DataFrame Object  
DataFrame as a specialized Dictionary  
The Pandas Index Object  
Data Indexing and Selection  
Data Selection in DataFrame  
DataFrame as twodimensional array  
Operating on Data in Pandas  
UFuncs Index Alignment  
Index alignment in DataFrame  
Ufuncs Operations Between DataFrame and Series  
Handling Missing Data  
Missing Data in Pandas  
NaN and None in Pandas  
Hierarchical Indexing  
The better way Pandas MultiIndex  
Methods of MultiIndex Creation  
MultiIndex for columns  
Indexing and Slicing a MultiIndex  
Rearranging MultiIndices  
Index setting and resetting  
Data Aggregations on MultiIndices  
Combining Datasets Concat and Append  
Duplicate indices  
Catching the repeats as an error  
Combining Datasets Merge and Join  
Specification of the Merge Key 
Specifying Set Arithmetic for Joins  
Aggregation and Grouping  
GroupBy Split, Apply, Combine  
Iteration over groups  
Aggregate, filter, transform, apply  
Transformation  
Pivot Tables  
Pivot Table Syntax  
Vectorized String Operations  
Methods using regular expressions  
Working with Time Series  
Dates and times in Pandas Best of both worlds  
Pandas Time Series Data Structures  
Using Functions  Introduction to Using Functions 
Standard Mathematical Functions  
time Functions  
Random Numbers  
Importing Issues  
Writing Functions  Function Basics 
Using Functions  
Main Function  
Parameter Passing  
Function examples  
Better Organized Prime Generator  
Command Interpreter  
Restricted Input  
Better Die Rolling Simulator  
Tree Drawing Functions  
Floating Point Equality  
Custom Functions vs Standard Functions  
More on Functions  Global Variables 
Default Parameters  
Recursion  
Making Functions Reusable  
Documenting Functions and Modules  
Functions as Data  
Lists  Using Lists 
List Assignment and Equivalence  
List Bounds  
Slicing  
Lists and Functions  
Prime Generation with a List  
List Processing  Sorting 
Flexible Sorting 
Search  
Linear Search  
Binary Search  
List Permutations  
Randomly Permuting a List  
Reversing a List  
Classes & Objects  Using Objects 
String Objects  
Level3(AI playing field and rules to play for it)  
Python Topics  Subtopic 
Introduction ML  Introduction 
What is Machine Learning?  
Examples of Machine Learning Applications  
Learning Association  
Classification  
Regression  
Introduction Stats & coding  Essential Libraries and Tools 
A First Application Classifying Iris Species  
Measuring Success Training and Testing Data  
First Things Fast – Look at your Data  
Building your first model kNearest Neighbors  
Making Predictions  
Supervised Learning with Python  Supervised Learning 
load_breast_cancer function from scikitlearn  
Data set analysis  
Linear Models  
Linear regression (aka ordinary least squares)  
Ridge regression  
Boston Housing dataset and evaluated LinearRegression  
Lasso  
Linear models for classification  
Decision boundaries of a linear SVM  
Coefficients Learned by the models with the three different settings  
Linear Models for Multiclass Classification  
Predictions for all regions of the 2D  
Strengths, weaknesses, and parameter  
Decision Trees  
Decision Trees  Decision Trees 
Introduction  
Univariate Trees  
Classification Trees  
Regression Trees  
Pruning 