Machine Learning
- Understanding Python & R
- Machine Learning Techniques
- Supervised Learning- I
- Supervised Learning – II
- Unsupervised Learning
- Advice for Applying Machine Learning
- Linear Regression with One Variable
- Linear Algebra Review
- Linear Regression with Multiple Variables
- Octave/Matlab Tutorial
- Logistic Regression
- Regularization
- Neural Networks: Representation
- Neural Networks: Learning
- Advice for Applying Machine Learning
- Machine Learning System Design
- Support Vector Machines
- Unsupervised Learning
- Dimensionality Reduction
- Anomaly Detection
- Recommender Systems
- Large Scale Machine Learning
- Machine Learning Use-Cases
- Machine Learning Process Flow
- Linear regression
- Gradient descent
- Dimensionality Reduction
- Association Rules Mining and Recommendation Systems
- Reinforcement Learning
- Time Series Analysis
- Model Selection and Boosting
- Hands-On Project
- Introduction to Graphical Model
- Bayesian Network
- Markov’s Networks
- Inference
- Model learning
- Introduction to Reinforcement Learning
- Markov Decision Processes and Bandit Algorithms
- Dynamic Programming & Temporal Difference Methods
- Value Function, Bellman Equation, Value Iteration, and Policy Gradient Methods
- In-class Project
- NLP with Python
- Introduction to Text Mining and NLP
- Extracting, Cleaning and Preprocessing Text
- Analyzing Sentence Structure
- Text Classification-I
- Text Classification-II
- Sentiment Analysis
- Machine Learning using Spark MLlib
- Data Preprocessing
- Feature Engineering
- Supervised Learning: Classification
- Time Series Modeling
- Ensemble Learning
- Recommender Systems
- Text Mining
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- Lectures 7
- Skill Level Professional
- Languages English
- Max downloads 127
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