CERTIFICATION COURSE

No. of Hours: 50

Certificate Validity: 2 Yr(s)

Python Initiation
1. Overview
2. Basic Syntax
3. Keywords & Identifiers
4. Comments
5. Input methods
6. Assigning values
7. Data types
8. Python Data types

Python Type Conversion, Decisions and Loops
1. Type Conversion
2. Decision making
a) If..
b) If..else
c) Nested if..else
d) If..elif..else
3. Loops
a) For loop
b) While loop
c) Nested loops

Python Handling Data-types
1. String
2. List
3. Tuple
4. Dictionary

Python Procedures
1. Function
2. Parameter passing
3. Types of Parameter passing
a) Call by Value
b) Call by Reference
4. Managing return types

Python Object Orientation
1. Classes
2. Methods
3. Importing Class
4. Access specifiers
5. Special Parameters
6. Constructor

Python File I/O Operations
1. Opening & Closing files
2. File Object Attributes
3. Modes of File
4. Reading & Writing files
5. Renaming & Deleting files
6. Directory operations

Python Exception Handling
1. Handling using Exception class
2. The try clause
3. The except clause
4. Except clause with No Exception
5. Except clause with Multi-Exception
6. Try-finally block
7. Argument of Exception
8. User Defined Exception

Python Package Operations
1. What is Package, Library, Module?
2. Installing Official Packages
3. Installing Third-party Packages
4. Installing using pip, apt and setup
5. Importing Packages as a whole
6. Importing selected module from Packages
7. Alias naming packages / modules / libraries

Python IDE
1. IDE Selection
2. Intro to Spyder, IDLE and Pycharm
3. Setting up IDE (Pycharm)
4. Execution methods in an IDE
5. Selective run and view
6. Highlighting mode (PEP 8) standards

Anaconda
1. Anaconda overview
2. Installing Anaconda
3. Setting up Anaconda for Python 2 & 3
4. Making Environment
5. Activating & Deactivating Environments
6. Setting up Conda environments in IDE

Working with Python IDE
1. Starting Projects with Environment
2. Installing Packages using Project Interpreter
3. Debugging in IDE
4. Viewing Docs for Packages

Machine Learning Basics
1. Datasets
2. Analysis of IV, DV
3. The Table Structure
4. Introduction to Pandas
5. Reading Datasets
6. Dataframe & Series
7. Accessing rows and columns
8. Accessing values in data cell
9. Operating on NaN values
10. Creating Pivots
11. Exporting Datasets
12. Introduction to numpy
13. Introduction to matplotlib.pyplot

Machine Learning Initiation
1. Data Pre-processing intuition
2. Importing Libraries
3. Importing Dataset
4. Handling Missing Data
5. Categorical Data
6. Training and Testing data
7. Feature Scaling

Machine Learning Algorithms

1. Regression
a) Simple Linear
b) Multi Linear
c) Polynomial
d) Support Vector
e) Decision Tree
f) Random Forest
g) Logistic

2. Classification
a) KNN
b) SVM

3. Clustering
a) KMeans
b) Hierarchical

4. Association Rule Mining
a) Apriori
b) Eclat

5. Reinforcement Learning
a) Upper Confidence Bound
b) Thompson Sampling

6. Natural Language Processing

Configuration:
1. Operating System: Ubuntu 16.04 LTS
2. RAM: 4 to 6 GB
3. Processor: i3 (Minimum)

Timings:
4 Hrs per Week (Saturday Preferred)
3 Months

Weekly Assignment:
1. After every weekend session you’ll need to solve assignments until next week.
2. On completion of 1 module, you are required to appear exam.
3. All exams will be taken either on Center / Off site. When Off-site you will submit assignments via mail.

Final Exam:
A duration of 7 days, will be given after completion of Course
The Final exam will be a conducted once, in case of Failure; exam can be reappeared after another 4 months

Opportunities:
As first Opportunity after Passing exams, you will deployed on a Well monitored Live Project.
You are required to resolve the Problem statement with Expected outputs, with that you will be receiving a token of Completion Letter of respective Project.