Learn the concept, syntax, semantics and gain introductory knowledge in Machine Learning principles through examples that provide useful exposure to the various concepts of Machine Learning.
Prerequisites: The tutorial is suitable for absolute beginners.
Utilities: Python, IDE-( PyCharm or Jupyter or Spider), Anaconda
Duration : 10 hours
Batch Date: 14th Oct’24 to 19th Oct’24
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.