Machine learning is a core sub-area of artificial intelligence as it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, computer programs, are enabled to learn, grow, change, and develop by themselves.
Machine learning is a core sub-area of artificial intelligence as it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, computer programs, are enabled to learn, grow, change, and develop by themselves.
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Basics of AI & Introduction:
Artificial Intelligence
Environmental Constraints
Various Agent Types
PEAS Analysis of Problem
CSP – Introduction
Process flow for an AI agent
Machine Learning Introduction
Supervised & Unsupervised Learning
Regression & Classification Problems
Fuzzy Logic:
Getting started with Fuzzy Logic
Applications of Fuzzy Logic
Problem Formulation, Defuzzification&Rulebase
Membership Functions
Defuzzification Methods
Mamdani&Sugeno Methods
Washing Machine Problem
Tipping Problem Analysis
Fuzzy Logic packages in Python
Using Pyfuzzy with python
Programming Fuzzy Logic Applications
Practical Examples, Case Studies & Hands on session on Fuzzy Logic
Linear Regression :
Regression Problem Analysis
Mathematical modelling of Regression Model
Gradient Descent Algorithm
Programming Process Flow
Use cases
Programming Using python
Building simple Univariate Linear Regression Model
Multivariate Regression Model
Boston Housing Prizes Prediction
Cancer Detection Predictive Analysis
Best Fit Line and Linear Regression
Decision Trees
Forming a Decision Tree
Components of Decision Tree
Mathematics of Decision Tree
Decision Tree Evaluation
Practical Examples & Case Study
Random Forest
Artificial Neural Networks
Neurons, ANN & Working
Single Layer Perceptron Model
Multilayer Neural Network
Feed Forward Neural Network
Cost Function Formation
Applying Gradient Descent Algorithm
Backpropagation Algorithm & Mathematical Modelling
Programming Flow for backpropagation algorithm
Use Cases of ANN
Programming SLNN using Python
Programming MLNN using Python
Digit Recognition using MLNN
XOR Logic using MLNN & Backpropagation
Diabetes Data Predictive Analysis using ANN
Project – Banking Problem Analysis – When the customer will leave?
Project – Medical Problem Analysis
Support Vector Machine
Concept and Working Principle
Mathematical Modelling
Optimization Function Formation
The Kernel Method and Nonlinear Hyperplanes
Use Cases
Programming SVM using Python
Character recognition using SVM
Regression problem using
Wisconsin Cancer Detection using SVM
Image Processing with Opencv
Image Acquisition and manipulation using opencv
Video Processing
Edge Detection
Corner Detection
Face Detection
Image Scaling for ANN
Training ANN with Images
Character Recognition
Clustering
Hierarchical Clustering
K Means Clustering
Use Cases for K Means Clustering
Programming for K Means using Python
Image Color Quantization using K Means Clustering Technique
Deep Learning Networks
Introduction to Tensor Flow
The Programming Model
Data Model
Tensor Board
Introducing Feed Forward Neural Nets
Softmax Classifier
ReLU Classifier
Dropout Optimization
Deep Learning Applications
Convolutional Neural Networks :
CNN Architecture
Pooling
Variants of the Basic Convolution Function
Efficient Convolution Algorithms
The Neuro-scientific Basis for Convolution Networks:
Natural Language :
Natural Language Processing & Generation
Semantic Analysis
Syntactic Analysis
Language Translation
Using NLTK
Using Textblob
Sentiment Analysis
Project: Streaming live tweets and Sentiment Analysis
Advice For applying Machine Learning:
Machine Learning for System Design:
Python Libraries used:
Numpy
Matplotlib
Pandas
Theano
Scikit-learn
Opencv
TensorFlow
Keras
Scikit-Image
Keras
Quandl
NLTK
Textblob