Machine Learning with Python

Curriculum

Session-1 : Machine Learning-Introduction

• Introduction of Machine Learning
• Evolution of Machine Learning
• Application of Machine Learning

Session-2 : Machine Learning-Fundamental

• Difference between Traditional Programming and ML Programming
• Requirements for Machine Learning Practical Implementation
• Required software and tools for Machine Learning Implementation
• Setup Anaconda
• Installation of Pycharm
• Configure Pycharm with Anaconda

Session-3 : Python-Basics

• Introduction of Python
• Features of Python
• Working with Python
• Basic Syntax of Python

Session-4 : Core Concepts of Python (Required for ML Practical Implementation )

• Loop
• List
• Tuple
• Dictionary
• String

Session-5 : Steps of Machine Learning Implementations

• Types of Machine Learning
• Labelled Data and UnLabelled Data
• Concept of Supervised Machine Learning
• Concept of UnSupervised Machine Learning
• Steps of Machine Learning
• Concept of Collecting the historic training Data for ML
• Concept of Preprocess data for ML
• Concept of Train the model
• Concept of Test the Algorithm and use it

Session-6 : Data Collection for Machine Learning

• Types of Data collection- Offline Data and Online Data
• Practical Implementation of Reading the offline dataset using Numpy
• Regression and Classification
• Linear Regression and Logistic Regression

Session-7 : Data Visualization for Machine Learning using Matplotlib

• Concept of Data Visualization and matplotlib
• Plotting Lines to represent the data for Machine Learning
• Plotting customized Lines for data representations
• Plotting scatter plots using matplotlib
• Plotting Stackplots using matplotlib
• Plotting Pie plots and etc using matplotlib

Session-8 : Practical implementation of Supervised Machine Learning Algorithm

• Implementation of Supervised Machine Learning Algorithms
• Practical Implementation of Machine Learning Supervised Algorithms- Linear Regression , Logistic Regression, Concept of Sigmoid Function

Session-9 : Practical implementation of Unsupervised Machine Learning Algorithm

• Concepts and Steps of Unsupervised Machine Learning Algorithm and Clustering
• Practical Implementation of Machine Learning UnSupervised Algorithms- K-Means Clustering.