Mode of Training:
Machine Learning using Python
- Python is used for Machine Learning as well: predicting stocks or spam detection, it is also used for Browser Automation. Actually you use selenium with Python to automate your browser to do social media posts.
- Python used in Artificial Intelligence: With AI, you can actually make a machine mimic the human brain which has the power to think, analyze and make decisions.
- C Language
- C++ / Java (Optional)
- Foundation of Python (Optional)
Module 1: Software Requirement Specification
- Installing Anaconda Navigator IDE (Python 3.6).
- Setting up class path of Python.
- Text Editor: Visual Studio Code.
Module 2: Basic Programs Handling Data Type
- Running python.
- Python identifier, keywords, comments etc.
- Assigning values to variable.
- Different data types in python.
- Python numbers, strings.
Module 3: Operators and Decision making in python
- Operators in python.
- Decision making in python.
- If elif else statement.
- Break and continue loops.
- While loops with else.
- For loop.
Module 4: 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.
- Default argument, keyword argument and arbitrary argument.
- Programs on function.
Module 5: Data Structure in python
- Lists and its different functions.
- List comprehension.
- Dictionary and tuples.
- Set and empty set.
- Pop and push on set.
- Using list as stack.
Module 6: Jupyter Overview
- Implementing Jupyter Notebook.
- Web Application embed Visualization.
Module 7: Machine learning packages in python
- Numpy package.
- Panda package.
- Matplotlib package.
- Sklearn package.
Module 8: Numerical and Computation using packages
- Creating Numpy array and manipulation.
- Indexing and slicing of Numpy array.
- Numpy operations.
Module 9: Python for Data Analysis – panda
- Series, Data frames, Group by, Merging Joining.
- Programs using python data frames.
- Data Manipulation with Panda.
Module 10: Python for Data visualization – Matplotlib
- Data visualization with Matplotlib.
- Scatter Plot, Straight Line Plot.
- Plotting of images.
Module 11: Introduction to Machine Learning using UCI Machine Learning Repository
- What are Features?
- What are Labels?
- Pre-processing of Data (mean max normalization).
- Over fitting and Under fitting of Model.
Module 12: Regression problem
- Linear regression.
- Logistic regression.
- Polynomial regression.
Module 13: Classification Problems
- Naïve Bayes.
- Decision Tree.
- Support Vector Machine.
Module 14: Clustering Problems
- Introduction to Un-Supervised Learning.
- K-Means Clustering.
Module 15: NLP
- Working with Text Data.
Module 16: Project
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