#### Abdur Rahman (Joy)

View Profile# Python for Data Science and Machine Learning (2nd Batch)

## Offline: TK 30000

**Start Date
: ** 2020-02-28

**End Date : ** 2020-07-28

**Total Class
: ** 40
**Total
Hours: ** 120

Location : D F Tower (Level-11A) Skill Jobs Digital Lab Skill Jobs Digital Lab, House # 11 (Level-11A), Road # 14, Dhanmondi, Shobhanbag, Dhaka-1209

**Friday**
: 06:00 PM
- 09:00 PM

**Saturday**
: 06:00 PM
- 09:00 PM

**Course Description**

Python is a general-purpose programming language that is becoming more and more popular for doing data science. Companies worldwide are using Python to harvest insights from their data and get a competitive edge. Unlike any other Python tutorial, this course focuses on Python specifically for data science. In our Intro to Python class, you will learn about powerful ways to store and manipulate data as well as cool data science tools to start your own analyses

**Data Science and Machine Learning **

This course will enable you to gain the skills and knowledge that you need to successfully carry-out real-world data science and machine learning projects.

The first part of the course covers data analysis and visualization. You will be working on real datasets using Python’s Numpy, Pandas, Matplotlib and Seaborn libraries.

The second part of the course focuses on machine learning. We will be covering both supervised and unsupervised learning. We will be working on case studies from a wide range of verticals including finance, heath-care, real estate, sales, and marketing. Some of the algorithms that will be discussed include Linear Regression, Logistic Regression, Support Vector Machines (SVM), and K-means clustering. This course is the foundation for Deep Learning courses in this specialization.

### Course Content

**Introduction **

**The Python Environment**

- Starting Python
- Using the interpreter
- Running a Python script
- Python scripts on Unix/Windows
- Editors and IDEs

**Getting Started **

- Using variables
- Built-in functions
- Strings
- Numbers
- Converting among types
- Writing to the screen
- Command-line parameters

**Flow Control**

- About flow control
- White space
- Conditional expressions
- Relational and Boolean operators
- While loops
- Alternate loop exits

**Lists and Tuples **

- About sequences
- Lists and list methods
- Tuples
- Indexing and slicing
- Iterating through a sequence
- Sequence functions, keywords, and operators
- List comprehensions
- Nested sequences

**Working with Files**

- File overview
- The with statement
- Opening a text file
- Reading a text file
- Writing to a text file

**Dictionaries and Sets**

- About dictionaries
- Creating dictionaries
- Iterating through a dictionary
- About sets
- Creating sets
- Working with sets

**Functions**

- About sequences
- Function parameters
- Global variables
- Global scope
- Returning values
- Sorting data

**Errors and Exception Handling **

- Syntax errors
- Exceptions
- Using try/catch/else/finally
- Handling multiple exceptions
- Ignoring exceptions

**Using Modules **

- The import statement
- Module search path
- Zipped libraries
- Creating Modules
- Function and Module aliases

**Classes**

- About o-o programming
- Defining classes
- Constructors
- Instance methods and data
- Class/static methods and data
- Inheritance

**Database**

- Database concepts
- Database design
- SQL
- Connecting Database with raw python

**Course Introduction **

Overview of Data Analysis, Data Visualization, and Machine Learning

**Environment Set-Up**

Jupyter Notebook Installation

**Python for Data Analysis – NumPy **

- Numpy Arrays
- Numpy Array Indexing
- Numpy Operations

**Python for Data Analysis – Pandas **

- Series
- Missing Data
- Group by
- Merging Joining and Concatenating
- Operations
- Data Input and Output

**Python for Data Visualization – Matplotlib **

Data Visualization with Matplotlib

**Python for Data Visualization – Seaborn**

- Distribution Plots
- Categorical Plots
- Matrix Plots
- Regression Plots
- Grid
- Style and Color

**Introduction to Machine Learning **

- What is machine learning?
- Supervised Learning
- Unsupervised Learning
- Machine Learning with Python

**Linear Regression **

- Model Representation
- Cost Function
- Gradient Descent
- Gradient Descent for Linear Regression
- Linear Regression with Python
- Linear Regression Project

**Cross-Validation and Bias-Variance Trade-Off **

Bias Variance Trade-Off

**Logistic Regression**

- Classification
- Hypothesis Representation
- Decision Boundary
- Cost function and Gradient Descent
- Logistic Regression with Python
- Logistic Regression Project

**K Nearest Neighbors **

- KNN Theory
- KNN with Python
- KNN Project

**Decision Trees and Random Forests**

- Introduction to Tree Methods
- Decision Trees and Random Forest with Python
- Decision Trees and Random Forest Project

**Support Vector Machines **

- Optimization Objective
- Kernels I and II
- Support Vector Machines with Python
- SVM Project

**K-Means Clustering**

- Optimization Objective
- Random Initialization
- Choosing the Number of Clusters
- K-Means with Python
- K-Means Project