Machine learning

Now we are living in the 5th generation of Computer revolution with Artificial Intelligence(AI). Computer devices with artificial intelligence are still in development, but some of these technologies are beginning to emerge and be used such as voice recognition. There is still a long way to go.

Machine learning evolves from artificial intelligence and the study of pattern recognition. Machine Learning in simple words, trains a machine to make decisions based on huge data from the past. So the goal is to build new algorithms, in order to build models that give accurate predictions or to find patterns, particularly with new and unseen similar data.
Require in sales forecasts, Accurate Medical Predictions and Diagnoses, image processing, etc.

Prerequisites
Participants are expected to know the basics of the C or python Programming Technique.

Module 1: INTRODUCTION TO PYTHON
Introduction to Python
Brief Overview of Python
Applications of Python
Advantages of Python
Overview Machine Learning with Python

Introduction to the Basics of Python Programming
Operators
Loops: while & for
Conditionals: if-else
Functions: Defining Functions, Anonymous Functions

Python Data Structure
Numbers
String
List
Tuple
Dictionary

Module 2: INTRODUCTION TO NUMPY
Importance of Array
Creation of Array
Array operation
Linear Algebra
Array Methods

Module 3: INTRODUCTION TO PANDAS
Pandas Series
Pandas Dataframe
Working with Structured & Unstructured Data
Accessing Tabular Data
Panadas methods

Data analysis using Pandas
Accessing Row & Column data
Data Filtering & Cleaning
Accessing indexed data
Indexing/Reindexing
Statistical Interpretation
Data Wrangling
Data Analysis
Store Dataframe to file

Module 4: INTRODUCTION TO DATA VISUALIZATION
Introduction to Matplotlib
Visualization of Array Data
Visualization of Excel Data
Different types of plotting
Line Graphs
Bar Plots, Histograms, Box Plot, Stacked Plots, Scatter Plot, Pie Chart

Data Analysis & Data Visualization using Matplotlib
Linestyles and Color
Multiple Lines on Same Plot
Controlling Line Properties
Adding Labels, Gridlines, Annotations
X and Y Ticks and Rotations
Legends
Working with Multiple Figures and Axes
Share X and Y-Axis
Adding Subplots

Module 5: INTRODUCTION TO MACHINE LEARNING

Part A: Introduction to Machine Learning
Application fields of Machine learning
Advantages of Python in Machine Learning
Steps towards Machine Learning
Understanding of Algorithms (Supervised & Unsupervised)
Feature Selection
Hyperparameter Tuning
Application and Implementation of Scikit Learn

Part B: Data Processing & Machine learning: Supervised Learning
Supervised Learning Introduction
Supervised Learning Algorithms
Regression
Linear Regression
Classification:
Logistic Regression
K-Nearest Neighbor
Naïve Bayes
Decision Tree
Random Forest

Part C: Data Processing & Machine learning: Unsupervised Learning
Unsupervised Learning Introduction
Unsupervised Learning Algorithms
Clustering: K-Means Clustering
Dimension Reduction
Principle Component Analysis

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