Machine learning using Python
Module 1: Introduction to Python
- History , environmental setting and installing python 3.6
- Installing anaconda IDE
- Overview of Jupyter, spyder, python command promt
- Running python
- Python identifier, keywords, comments etc
- Assigning values to variable
- different data types in python
- python numbers, strings
Module 2: Operators and Decision making in python
- operators in python
- decision making in python
- if elif
- break and continue
- loops
- while loop with else
- for loop
Module 3: Functions in python
- defining a function with ‘def’ keyword
- calling a function in python
- pass by value and pass by reference
- local vs global variable
- modules and packages in python
- lambda expression
- default argument, keyword argument and arbitrary argument
- programs on function
Module 4: Data Structure in python
- lists and its different functions
- list comprehension
- dictionary and tuples
- different functions of dictionary, tuple
- Set and empty set
- Pop and push on set
- Using list as stack
- Using list as Queue
- Difference between all the above data structure
Module 5: Introduction to AI and Machine learning
- Application of AI
- Deep learning, neutral learning, machine learning
- Introduction to Machine learning
- Supervised learning, (labelled data sets)
- Unsupervised learning, (not labelled)
- Reinforcement learning.
Module 6: Machine learning packages in python
- Introduction to Numpy package
- Creating numpy array
- Indexing and slicing of numpy array
- Numpy operations
- Introduction to sci-py
- Introduction to scikit-learn
Module 7 : Python for Data Analysis - panda
- Introduction to pandas
- series and dataframes
- programs using python data frames
Module 8: Python for Data visualization - Matplotlib
- introduction to data visualization and Matplotlib
- data visualization with Matplotlib
Module 9: regression problem
- introduction to regression problem
- linear regression
- logistic regression, polynomial regression etc
- k-nearest Neighbors