Machine Learning

Machine Learning is revolutionizing the way we interact with technology by enabling computers to learn from data and make intelligent decisions. It's at the heart of innovations like self-driving cars, personalized recommendations, and advanced medical diagnoses. Learning Machine Learning equips you with the skills to build predictive models, uncover patterns in data, and automate complex tasks. With applications spanning finance, healthcare, marketing, and beyond, mastering Machine Learning opens up endless opportunities to impact the world. Start your journey into Machine Learning today and be at the forefront of the AI-driven future

Course Content

Introduction to Data Science

Data Science with real-world Examples

Applications using Data Science

Applications using Machine Learning

Applications using Deep learning

Applications using computer vision

Applications using Gen AI

Data types

varibales

operators

print

precision and fied width

Python Installation and Running

Installation and Running

Jupyter Notebook

.py file from terminal

Google Colab

String

list

tuple

sets

dictionary

Forloops

while loops

if

else

elif

break

continue

functions

lambda function

comprehensions

Error / Exception Handling

File Handling

JSON module

OS Module

Pickle Module

Datetime Module

Copy Module

oops concept

class

object

constructor

inheritance

polymorphisam

abstraction

excapsulation

what is process

what is multiprocessing

what is multithreading

start

join

kill

terminate

Matrix Algebra

Vector Matrix

multiplication Matrix

Eigen Values and Eigen vectors

Regression lines etc

Numpy

Pandas

Data Visualization Library: Matplotlib

Seaborn

Descriptive Statistics

Measure of central tendency

Measure of dispersion

outliers

covariance

correlation

testing

hypothesis testing

mean

median

mode etc..

Conditional Probability

Bayes Rule

Probability Distribution: Discrete and Continuous

Normal Distribution etc..

Types of Machine Learning Methods

Supervised Machine learning

Unsupervised Machine learning

Reinforcement Machine learning

Linear Regression

Simple Linear Regression

Multiple Linear Regression

Polynomial Linear Regression

Loss Function

MSE

MAE

RMSE

Optimizers

Gradient Descent

Regularization Parameters

L1 and L2 Regularization

Mini Project - 1 Developing Regression Model to find car cost status

Mini Project -2 finding best Regresson Line using Gradient Descent Optimizers

KNN

Logistic Regression

Naive Bayes

Decision Tree

Ensemble: Bagging Random Forest Classifier

Ensemble: Boosting Gradient Boosting

AdaBoost

XG Boosting

Support Vector Machine (SVM)

Creation of new features from existing data

Selection of relevant features for the model

Handling missing values

Handling categorical data

Normalization and scaling

Encoding categorical variables

Polynomial features

Log transformations

Date-time feature extraction

Domain-specific transformations

Dimensionality reduction techniques

Text feature extraction

Image feature extraction

Feature crossing

Feature splitting

Handling outliers

Feature discretization

Sequence feature extraction

Time-series feature extraction

Feature standardization

Feature normalization

Filter methods

Wrapper methods

Embedded methods

Univariate feature selection

Recursive feature elimination (RFE)

Feature importance from models

L1 regularization (Lasso)

L2 regularization (Ridge)

Variance thresholding

Mutual information

Chi-square test

Correlation matrix

Principal component analysis (PCA)

Feature selection with cross-validation

Sequential feature selection

Information gain

Gain ratio

SelectFromModel

SelectKBest

SelectPercentile

Clustering

Dimensionality reduction

Anomaly detection

Association analysis

Density estimation

Feature learning

Similarity measurement

Data preprocessing

Visualization of high-dimensional data

Manifold learning

Centroid initialization

Number of clusters (k)

Euclidean distance

Cluster assignment

Centroid update

Inertia (within-cluster sum of squares)

Elbow method

Silhouette score

K-means++ initialization

Convergence criteria

Scalability

Handling large datasets

Assumption of spherical clusters

Sensitivity to outliers

Agglomerative clustering

Divisive clustering

Dendrogram

Linkage criteria

single

complete

average

Distance metrics

Euclidean

Manhattan

Merging clusters

Determining the number of clusters

Cutoff threshold

Hierarchical tree structure

Visualization of clusters

Scalability

Interpretability

Dimensionality reduction

Principal components

Eigenvalues

Eigenvectors

Covariance matrix

Explained variance

Projection of data

Orthogonal transformation

Scree plot

Cumulative explained variance

Standardization of data

Interpretation of principal components

Data compression

Noise reduction

Density-based clustering

Core points

Border points

Noise points (outliers)

Epsilon (ε) parameter

MinPts parameter

Density reachability

Density connectivity

Cluster formation

Handling of noise

Ability to find arbitrarily shaped clusters

Scalability

Sensitivity to parameter selection

Computational complexity