Online Live Training: Data Science & Machine Learning With Python

This training is jointly organized by BITM & LEADS Training & Consulting Ltd

Training will be held in LEADS Training & Consulting Ltd.

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

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

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

Using Modules  

• The import statement
• Module search path

Classes 

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

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

K Nearest Neighbors  

• KNN Theory
• KNN with Python
• KNN 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