Training & Duration
- Live classes (Monday to Friday)
- 4 Weeks of Training & 2 Weeks of Project Work
Course Features
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- Online lectures: Online live lectures.
- Updated Quality content: Content is the latest and gets updated regularly to meet the current industry demands.
Test & Evaluation
1. During the program, the participants will have to take all the assignments given to them for better learning.
2. At the end of the program, a final assessment will be conducted.
Certification
1. All successful participants will be provided with a certificate of completion.
2. Students who do not complete the course / leave it midway will not be awarded any certificate.
Basic knowledge of any programming language will be helpful
Course Outline
1: Introduction
- What is Python..?
- A Brief history of Python
- Why Should I learn Python..?
- Installing Python
- How to execute Python program
- Write your first program
2: Variables & Data Types
- Variables
- Numbers
- String
- Lists ,Tuples & Dictionary
3: Conditional Statements & Loops
- if...statement
- if...else statement
- elif...statement
- The while...Loop
- The for....Loop
4: Control Statements
- continue statement
- break statement
- pass statement
5: Functions
- Define function
- Calling a function
- Function arguments
- Built-in functions
6: Modules & Packages
- Modules
- How to import a module...?
- Command line arguments
- Packages
- Creating custom packages
7: Classes & Objects
- Introduction about classes & objects
- Creating a class & object
- Inheritance
- Methods Overriding
- Data hiding
8: Files & Directories
- Writing data to a file
- Reading data from a file
- Working with csv file
- The os module
- Working with files and directories
9: Introduction to Sqlite database
- Overview
- Create Database
- Create Table
- Drop Table
- Insert query
- Select query
- Delete and Update query
- WHERE, AND & OR Clause
10: Regular Expression
- Need of regular Expressions
- re module
- Functions /Methods related to regex
- Meta Characters & Special Sequences
Data Analysis and Visualization
11: Working with N-dim arrays:NumPy
- Introduction to NumPy
- Creating an array
- Class and Attributes of ndarray
- Basic Operations
- Activity-Slice
- Stack operations
- Mathematical Functions of NumPy
12: Data Analysis using Pandas
- Understanding DataFrame
- Series
- Concatenating and appending DataFrames
- loc and iloc
- Drop columns or rows
- Groupby
- Map and apply
- Dealing with missing data
- Handling categorical data
- Encoding class labels
- One-hot-encoding
13: Data visualization:Matplotlib / seaborn
- Overview
- Scatter plot, line plot, bar plot
- Histogram
- Xlabel,Ylabel,Xticks,Yticks,title
- Marker style,type, size
- Figure and Subplot
- Saving a Figure
- HeatMap,BoxPlot
Predictive Modeling using scikit-learn
14: K-Nearest Neighbors(KNN)
- KNN theory
- Implementing KNN with scikit-learn
- KNN Parameters
- n_neighbors
- metric
- How to find Nearest Neighbors
15: Model Evaluation and Parameter Tuning
- Cross validation via K-Fold
- Tuning hyperparameters via grid search
- Confusion matrix
- Recall and Precision
- ROC and AUC
16: Decision Tree and Random Forest
- Theory behind decision tree
- Implementing decision tree with scikit-learn
- Decision tree parameters
- Combining multiple decision trees via Random forest
- How random forest works..?
17: Clustering and Dimension Reduction
- K-means Clustering
- Hierarchical Clustering
- Elbow method
- Principal components analysis(PCA)
- PCA step by step
- Implementing PCA with scikit-learn
For inquiry call: 8953463074