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Advanced Machine Learning

Instructor led best course for learning advance machine learning and developing deep learning models using real world case studies.

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4.7

Rating

35 Hrs

Duration

Intermediate

Level

30

Assignments

What you'll learn

Ensemble modeling techniques like bagging and boosting
Soft clustering and Density based clustering
Neural network architecture and building deep learning models
Reinforcement learning architecture and approach

Who should take this course

Anyone with prior knowledge in python programming and Machine Learning
Anyone who wants to build deep learning models for AI applications

Skills you'll gain

Ensemble modeling
Bagging
Boosting
Soft clustering
Density based clustering
Neural Network
MLP
RNN
CNN
Reinforcement Learning

Course contents

Benefits
Types: Bagging and Boosting

Understanding the case study
Data preprocessing
Assumptions of Random Forest
Defining the cost function
Training the model using python sk-learn
Error analysis and performance metrics

Understanding the case study
Data preprocessing
Assumptions of XGBoost
Defining the cost function
Training the model using python xgboost
Error analysis and performance metrics

Understanding the case study
Data preprocessing
Assumptions of SVM
Defining the cost function
Training the model using python sk-learn
Error analysis and performance metrics

Understanding the case study
Data preprocessing
Assumptions of GMM
Defining the cost function
Training the model using python sk-learn
Error analysis and performance metrics

Understanding the case study
Data preprocessing
Assumptions of DBSCAN
Defining the cost function
Training the model using python sk-learn
Error analysis and performance metrics

Overview and types of Neural Networks
Architecture of single layered perceptron
Cost function for Regression and classification
Learning mechanism
Limitations

Multilayer perceptron (MLP) architecture
Back propagation algorithm
Defining the cost function
Training the model using TensorFlow/ PyTorch
Error analysis and performance metrics

RNN architecture
Backpropagation through time algorithm
Limitation of RNN
LSTM architecture
Using LSTM in a time series forecasting use case
Defining the cost function
Training the model using TensorFlow/ PyTorch
Error analysis and performance metrics

CNN architecture
Using CNN for object detection use case
Defining the cost function
Training the model using TensorFlow/ PyTorch
Error analysis and performance metrics

SOM architecture
Using SOM for anomaly detection use case
Defining the cost function
Training the model using TensorFlow/ PyTorch
Error analysis and performance metrics

Overview of RL
Advantages and Disadvantages
Architecture of RL system
Learning process
Case study: Applying RL in recommender system

Advanced NLP

Here Semantic embeddings, Information retrieval, Text summarization, Language modelling and recommendation engine concepts will be taught.

Image processing and Computer vision

Here advance concepts like Deep learning, Digital Image processing, Object detection, Handwriting recognition and Image captioning are taught in details.

Advanced Python programming

This course will take individual to expert level python coding. It includes recursive functions, OOPs, Generators, Decorators, Multi processing, Unit testing and backend web development.