Description

An AI (ML) designer is a specialist in utilizing information to preparing models. The models are then used to computerize measures like picture characterization, discourse acknowledgement, and market estimating. Meanings of AI jobs can fluctuate. Regularly there is some reasonable cover or even conflation with information researcher or human-made consciousness (AI) engineer. AI is a subfield of AI that centres on dissecting information to discover relations between the information and the ideal yield. An AI designer creates a customized answer for every issue. The best way to accomplish ideal outcomes is to deliberately handle the information and select the given setting's best calculation.

Syllabus

Beginners

• Unit-1 Basics of Machine Learning
  • What is Machine Learning
  • How Machine Learning works
  • Features of Machine Learning
  • Need for Machine Learning
  • Classification of Machine Learning
• Unit-2 Methods for Machine Learning
  • Types of Methods
  • Batch Learning
  • Online Learning
  • Generalization Approach
  • Clustering
  • Association
  • Dimensionality Reduction
  • Anomaly Detection
• Unit-3 Understanding Data with Statistics
  • Statistical Features
  • Basic Box Plot
  • Probability Distributions
  • Uniform Normal Possion
  • Dimensionality Reduction
  • Over and Under Sampling
  • Bayesian Statistics
  • Bay's Theorem
• Unit-4 Understanding Data with Visualization
  • Data Pre-Processing
  • Importance of Data Pre-Processing
  • Getting Started with Data Pre-Processing
  • Box-Plots
  • Joint Plot
  • Bar Chart

Intermediate

• Unit-5 Supervised Learning
  • Steps Involved in Supervised Learning
  • Types of Supervised Machine Learning Algorithms
  • Regression
  • Linear Regression
  • Non-Linear Regression
  • Bayesian Rectilinear Regression
  • Regression Trees
  • Polynomial Regression
• Unit-6 Unsupervised Learning
  • Introduction - Clustering, Association
  • Importance of Unsupervised Learning
  • Types of Unsupervised Learning Algorithm
  • Types of Unsupervised Learning Algori-Clustering
  • Types of Unsupervised Learning Algori-Association
  • Advantages of Unsupervised Learning
  • Disadvantages of Unsupervised Learning
  • Clustering
• Unit-7 Logistic Regression and Sigmoid Probability
  • Properties of Logistic Regression
  • Classification
  • Types of Classification Algorithms
  • Logistic Regression
  • Sigmoid Probability
  • K-nearest Neighbors
  • KNN-Classification
  • SVM Classification
  • Naive Bayes Classifier
  • Bayes Theorem
  • CART Algorithms
  • Model Development and Prediction
• Unit-8 Decision Tree
  • Decision Tree
  • The expressiveness of Decision Trees
  • Decision Tree Boundary
  • Decision Tree Learning Algorithm
  • Pseudo Code
  • Calculating Information Gain

Advanced

• Unit-9 Naive Bayes Classifier Algorithm
  • Bayes Theorem
  • Naive Bayes Algorithm
  • Maximizing a Posteriori
  • Feature Distribution
• Unit-10 Linear Regression Algorithms
  • Ordinary Least Squares
  • Regularization
  • Preparing Data For Linear Regression
• Unit-11 Artificial Neural Networks
  • What is ANN
  • Artificial Neural Networks Architecture
• Unit-12 Support Vector Machine Algorithms
  • Support Vector Machine
  • Problem Statement

Professional

• Unit-13 What is Clustering
  • Uses of Clustering
  • Generalization
  • Data Compression
  • Privacy Preservation
  • Types of Clustering
• Unit-14 K-Mean Clustering
  • What is K-means Clustering
  • K-Means in Action
• Unit-15 Hierarchical Clustering
  • Steps to Perform Agglomerative Hierarchical Clustering
• Unit-16 Improving
  • Improving
  • Treat missing and Outlier values
  • Feature Engineering
  • Multiple algorithms
  • Ensemble methods
  • Cross-Validation