Machine Learning
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Machine Learning
Machine Learning Duration: 40 hrs
This course provides advanced-level training on Machine Learning applications and algorithms. It will give you hands-on experience in both supervised and unsupervised learning, machine learning algorithms like regression, clustering, classification, and recommendation.
Course Objective: This Course covers supervised and unsupervised learning techniques, regression and classification problem analysis, artificial neural networks, backward propagation, SVM and Natural language processing
Prerequisite: Basic Understanding of Python /MATLAB programming language and overview of windows/Linux operating system
Module 1
- Getting started with the programming language
- Lab: working with functions, control structure etc.
- Artificial Intelligence
- Current Applications & Advancements
- Machine Learning & Applications
- Future Advancements and Current Trend
- Popular Algorithms
- Supervised & Unsupervised Learning Technique
- Regression & Classification Problem Analysis
- Artificial Neural Networks
- Linear Algebra review
- Introduction to Neuron
- Introduction to Network Architecture
- Designing Neural Network Model
- Model Representation Methods
- Weights & Activation Functions
- Gradient Descent Algorithm
- Single Layer Perceptron Model
- Lab: Single Layer Perceptron Model Learning
- Lab: Housing Prizes Prediction
- Multilayer Neural Network Architecture
- Training the Network
- Backward Propagation Training
- Lab: Multilayer ANN Training Example
Best Mean Fitting
- Working with Best Mean Fitting
- Lab: Single Line as Hypothesis Training
Module 2
Machine Learning – SVM & Natural Language Processing
- Introduction to SVM
- Decision Boundary and Hyper plane
- Minimization of Cost function for optimized hyperplane
- Support Vector for Classification – Parameters
- Support Vector for Regression – Parameters
Case study & Hands On (Labs):
- Character Recognition
- Cancer Recognition
- Regression Problem Example
Natural Language Processing (Different Toolbox in case of MATLAB)
- Working with Sklearn, nltk python library for NLP
- Accessing External Data, working with words and sentences
- Lab: Using NLTK for extracting data
- Lab: Sentiment Analysis Example
- Lab: Internet Data Analysis
- Lab:Color Recognition, shape recognition &Real Time camera data Analysis
- Lab: Character recognition, object recognition
Unsupervised Learning
- Data Clustering methods
- Multiple clusters
- K Means clustering method
- Nearest neighbor clustering
- PCA Algorithm
- PCA & K Means Clustering
- Applications in Self Driving