Certificate IN DATA SCIENCE AND ANALYTICS SYLLABUS – PYTHON AI /ML ( S-SICT-PYTHON AI / ML )

1st Week Python

1.       Installation of Python

2.       Installation of Anaconda, IDLE and Jupyter notebook

3.       History and Introduction of Python

4.       Application of Python

5.       Why we choose Python for Data Science and AI?

6.       Types of values

7.       Print (), type (), len() methods,

8.       String operations

9.       Input and output

10.   Using str in input

11.   Type of input i.e int, float

12.   Use of eval function

13.   Data structures in Python

14.   List operations, methods

15.   List comprehensions

16.   Tuple methods like index, count

17.   Differentiation between list and tuple

2nd Week Python

1.       Set

2.       Dictionary

3.       Del statement

4.       Control statements

5.       If, if_else

6.       Elif

7.       Range function

8.       Loops in Python

9.       For loop

10.   While loop

11.   Infinte loop

12.   Functions

13.   Types of functions

14.   In-build and User defined

15.   What are functions?

16.   Arguments

17.   Types of arguments

18.   Argument and keyword argument

19.   Default argument

20.   Return statement

21.   Lambda expression

22.   Filter function

23.   Map function

3rd Week Advance Python

1.       Advanced Python (Libraries)

2.       NumPy

3.       Pandas

4.       EDA using Pandas

5.       Matplotlib

6.       Seaborn

7.       Plots using Matplotlib and Seaborn

·         File Handling

·         Error Handling

·         What are errors and exceptions?

·         Classes and objects

·         How to create an object of a class?

·         Inheritance

·         Multiple inheritance

·         Polymorphism

·         Encapsulation

·         Access modifiers - Public, private and protected

·         Iterators and generators

4th Week Machine Learning

1.       Introduction of Machine learning

2.       Real life application of ML

3.       Features of ML

4.       Types of ML

5.       Supervised learning

6.       Unsupervised Learning

7.       Reinforcement learning

8.       Machine learning algorithms

5th Week Artificial Intelligence

1.       Introduction of AI and Deep learning

2.   Application of AI and DL

3.       Neural network and it's components

4.       CNN

5.       RNN

6.       Tensorflow

7.       Introduction to NLP