A perfect hands-on practice for beginners to who want to elevate their machine learning skills.
A perfect hands-on practice for corporate developers to who want to delve into machine learning area.
A perfect hands-on practice for academicians to who want to delve into machine learning area.
"Applied Machine Learning in Python"
Key Features:
- 30 Hours of theory classes, 30 hours of Lab along with theory classes.
- A strong theoretical foundation step-by-step on various learning algorithms
- Hands-on coding skill development in Python.
- End-to-End 6 Academic/Research projects using ML models in major research areas like Text Analytics, Image Processing & Speech Analytics
Overview:
This training course is for people that would like to apply Machine Learning in practical applications.
Audience:
This course is for developers and academicians that have some familiarity with statistics and know how to program (preferably Python).
Prerequisites:
- Acquaintance with Python Programming Language
- Little knowledge about Linear algebra (Matrices) and Probability theory.
Note: Each session having 1 Hour.
Session 1- Introduction
- What is machine learning?
- Algorithm types of Machine learning
- Supervised and Unsupervised Learning
- Uses of Machine learning
- Planning for ML techniques
Session 2,3,4,5 - Linear Regression with Multiple variables
- Model Representation
- Cost Function
- Gradient Descent Optimization Method
- Gradient Descent For Linear Regression
Session 6,7,8 - Advice for applying Machine Learning
- Model Selection
- Train/Validation/Test Sets
- Evaluating a hypothesis
- K-Cross fold validation, Stratified K-cross
- Machine Learning System Design
Session 9,10,11,12,13,14,15 - Supervised Learning
- Decision Trees
- Random forest
- Gradient boosting Machines
- Support Vector Machines
Session 16,17,18,19,20,21- Unsupervised Learning
- Clustering Technique
- K-nearest neighbor
- Clustering - K means
- Distance Measure and Data Preparation – Scaling
- Evaluation and Profiling of Clusters
- Other types of Clustering
- Dimensionality Reduction
- Data Compression/Dimensionality Reduction - Introduction
- Principal Component Analysis
- Linear Discriminant Analysis
Session 22, 23,24,25 - Probabilistic Classifiers
- Naive bayes classifier
- Gaussian Mixture Models
Session 26, 27, 28, 29, 30 - Nueral Network/Deep Learning
- Neural Networks : Introduction
- Multi-layer Perceptron
- Deep Learning and its applications
End-to-End Academic/Research Projects:
Natural Language Processing (Week 1)
- Email Spam Filtering
- Sentiment analysis on Movie Reviews
Image Processing (Week 2)
- Image Compression using K-means clustering
- Digit Recognition on MNIST Database using MLP & DL
Speech Processing (Week 3)
- Voice Gender Detection
- Speaker Identification in Speech