Programming Concepts

Python Basics

Variables and Data types in Python

Operators and Strings in Python

Python Data Structures

Control Flow Statements and Functions

OOPs

NumPy

Pandas

Probability

Statistics

Normal Distribution

Sampling Distribution

Hypothesis

Central Limit Theorem

Advanced Statistics

Hypothesis testing

Type-I and Type-II error

Introduction of Machine Learning

Supervised and Unsupervised Machine Learning

Regression

Classification

Recommendation System

ML on Cloud

Python

Statistics

Reinforcement learning

Machine learning

Fundamentals of Artificial Intelligence

Neural networks

Basics of Natural Language Processing (NLP)

Fundamentals of Computer Vision

Tasks involved in Computer Vision

Forecasting using Python

Exponential Smoothing

ARIMA

Time Series in R

Introduction to Machine Learning

Understanding the ML Pipeline

Data Preparation

Formatting Data

Data Transformation

Building ML models

Analyzing ML models

Types of Linear Regression

Regression analysis

Missing Value Detection

Data handling and prediction

Scikit Learn Library

Logistic Regression

Naïve Bayes

Entropy

Heterogeneity

Shannon's Entropy

Preventing Overfitting

Random Forest

Random Forest Regression

Hands-on

Logistic Regression vs Random Forest

Linear Regression vs Random Forest

Unsupervised Learning

Clustering

k-means Clustering

Introduction to Hierarchical Clustering

Agglomerative Hierarchical Clustering

Euclidean Distance

Manhattan Distance

Minkowski Distance

Jaccard Index

Cosine Similarity

Optimal Number of Clusters