Beginning Python for Machine Learning

Beginning Python for Machine Learning

This is a step by step course designed for learners without any prior python or programming knowledge. This is also useful for those who are experienced with other programming languages but not python in particular. There are also those learners who know python but the skills are either rusty or not too familiar with the recent changes in python’s syntax. This course is also very useful for them.

The objective of learning python in this course is to be able to start using python for Machine Learning.

Duration

3 days

Learning Outcome

• Overview of python basics.
• Understand the basic syntax structures
• Code in python
• Develop standalone modules using python
• Ability to learn added python modules and sub systems
• Python for Data Science
• Learn how machine learning works
• Understand different types of machine learning
• Supervised Machine Learning
• Unsupervised

Prerequisites

• Access to PC [either one of these OS: MS Windows / Mac / Linux (ubuntu)]
• Basic understanding of filing system
• Internet connection
• Very basic understanding of server architecture
• Basic education in mathematics (understanding of linear and non-linear mathematics at high-school level)
• Google account or administrative / root access to PC

Course Outline

1. BASIC PYTHON SYNTAX
   1. Identifiers
   2. Reserved Words
   3. Lines and Indentation
   4. Comments
2. VARIABLES AND COLLECTIONS IN PYTHON
   1. Numbers
   2. Python Lists
   3. Python Tuples
   4. Python Dictionaries
   5. Python Sets
   6. Copying
   7. Python Strings
   8. String formatting
3. PYTHON OPERATORS
   1. Arithmetic Operators
   2. Comparison (Relational) Operators
   3. Assignment Operators
   4. Logical Operators
   5. Membership Operators
   6. Operators Precedence
4. LANGUAGE COMPONENTS
   1. Decision Making
   2. The if Statement
   3. The if else Statement
   4. For Loop
   5. While Loop
   6. Break And Continue
5. FUNCTIONS IN PYTHON
   1. Defining Your Own Functions
   2. Parameters
   3. Function Documentation
   4. Passing Collections to a Function
   5. Variable Number of Arguments
   6. Scope
   7. Map
   8. Filter
   9. Lambda
6. DATA IN PYTHON
   1. Python Numpy Arrays
   2. Matrix and its operation
   3. Scope in Python
   4. Pandas and Data frames
   5. Import and Export data from CSV
7. FUNDAMENTALS OF MACHINE LEARNING
   1. What is Machine Learning?
   2. Process of Machine Learning
   3. Life Cycle of Machine Learning
   4. Application working in Machine Learning
   5. Types Of Machine Learning
8. DATA PREPROCESSING
   1. Importing Libraries
   2. Importing Dataset
   3. Taking care of Missing Data
   4. Encoding Data: Categorical Data
   5. Splitting the dataset into the Training set and Test set
   6. Feature Scaling
9. REGRESSION
   1. Simple Linear Regression
   2. Multiple Regression
   3. Polynomial Regression
10. CLASSIFICATION
    1. Implementation of Logistic Regression
    2. Implementation of Naive Bayes
11. CLUSTERING
    1. K-means Clustering
    2. K-means Selecting the Number of Clusters
    3. Implementation of k-means clustering
12. CONCLUSION