Course Outline
Introduction
This section offers a foundational overview of when to apply 'machine learning,' key considerations, and the broader context, including advantages and disadvantages. It explores data types (structured, unstructured, static, and streamed), data validity and volume, the distinction between data-driven and user-driven analytics, and the differences between statistical and machine learning models. Additionally, it addresses challenges in unsupervised learning, the bias-variance trade-off, iterative evaluation, cross-validation strategies, and the paradigms of supervised, unsupervised, and reinforcement learning.
MAJOR TOPICS
1. Understanding Naive Bayes
- Foundational concepts of Bayesian methods
- Probability theory
- Joint probability
- Conditional probability and Bayes' theorem
- The Naive Bayes algorithm
- Classification using Naive Bayes
- The Laplace estimator
- Incorporating numeric features into Naive Bayes
2. Understanding Decision Trees
- The divide and conquer approach
- The C5.0 decision tree algorithm
- Selecting optimal splits
- Pruning decision trees
3. Understanding Neural Networks
- Evolution from biological to artificial neurons
- Activation functions
- Network topology
- Determining the number of layers
- Direction of information flow
- Sizing the number of nodes per layer
- Training networks via backpropagation
- Deep Learning
4. Understanding Support Vector Machines
- Classification using hyperplanes
- Identifying the maximum margin
- Handling linearly separable data
- Handling non-linearly separable data
- Utilizing kernels for non-linear spaces
5. Understanding Clustering
- Clustering as a machine learning objective
- The k-means clustering algorithm
- Using distance metrics for cluster assignment and updates
- Determining the optimal number of clusters
6. Measuring classification performance
- Processing classification prediction data
- Analyzing confusion matrices
- Assessing performance with confusion matrices
- Performance metrics beyond accuracy
- The kappa statistic
- Sensitivity and specificity
- Precision and recall
- The F-measure
- Visualizing performance trade-offs
- ROC curves
- Predicting future performance
- The holdout method
- Cross-validation
- Bootstrap sampling
7. Optimizing standard models for enhanced performance
- Automated parameter tuning with caret
- Developing simple tuned models
- Customizing the tuning workflow
- Boosting model performance via meta-learning
- Concepts of ensembles
- Bagging techniques
- Boosting techniques
- Random forests
- Training random forests
- Evaluating random forest performance
MINOR TOPICS
8. Understanding classification via nearest neighbors
- The kNN algorithm
- Distance calculation
- Selecting an appropriate k value
- Data preparation for kNN
- The lazy nature of the kNN algorithm
9. Understanding classification rules
- The separate and conquer strategy
- The One Rule algorithm
- The RIPPER algorithm
- Deriving rules from decision trees
10. Understanding regression
- Simple linear regression
- Ordinary least squares estimation
- Correlations
- Multiple linear regression
11. Understanding regression trees and model trees
- Integrating regression into tree structures
12. Understanding association rules
- Apriori algorithm for association rule learning
- Measuring rule interest: support and confidence
- Generating rule sets using the Apriori principle
Extras
- Spark, PySpark, MLlib, and Multi-armed bandits
Requirements
Knowledge of Python
Testimonials (7)
I thoroughly enjoyed the training and appreciated the deeper dive into the subject of Machine Learning. I appreciated the balance between theory and practical applications, especially the hands-on coding sessions. The trainer provided engaging examples and well-designed exercises that enhanced the learning experience. The course covered a wide range of topics, and Abhi demonstrated excellent expertise by answering all questions with clarity and ease.
Valentina
Course - Machine Learning
I appriciated the exercise that help me to undersand the theory and apply it step by step . as well the way the trainer explained everything in a simple and clear manner. It was easy to follow even though I'm not very experienced with Python, still, I didn't want to miss the opportunity to learn something that relly interests me. I also appreciated the variety of information provided and the trainer’s availability to explain and support us in understanding the concepts. After this course, machine learning concepts are much clear to me, and now I feel like I have a direction and a better undersantind of the topic.
Cristina
Course - Machine Learning
At the end of the training, I could see the real-life use-case of the subjects presented.
Daniel
Course - Machine Learning
I liked the pace, I liked the balance between theory and practice, the main topics covered and the way the trainer was able to put everything into balance. I also really like your training infrastructure, very practical to work with VMs
Andrei
Course - Machine Learning
Keeping it short and simple. Creating intuition and visual models around the concepts (decision tree graph, linear equations, calculating y_pred manually to prove how the model works).
Nicolae - DB Global Technology
Course - Machine Learning
It helped me achieve my goal of understanding ML. Much respect for Pablo for giving a proper introduction in this topic, since it becomes obvious after 3 days of training how vast this topic is. I have also enjoyed A LOT the idea of virtual machines you have provided, which had very good latency! It allowed every coursant to do experiments at their own pace.
Silviu - DB Global Technology
Course - Machine Learning
The way practical part, seeing the theory materializing into something practical is great.
