Course Outline
Introduction
This section offers a broad overview of when to employ 'machine learning,' outlining key considerations, benefits, and drawbacks. It explores data types (structured/unstructured/static/streamed), data integrity and volume, the distinction between data-driven and user-driven analytics, and comparisons between statistical and machine learning models. It also addresses unsupervised learning challenges, the bias-variance trade-off, iteration and evaluation, cross-validation strategies, and the concepts of supervised, unsupervised, and reinforcement learning.
KEY TOPICS
1. Mastering Naive Bayes
- Foundational concepts of Bayesian methods
- Probability
- Joint probability
- Conditional probability via Bayes' theorem
- The Naive Bayes algorithm
- Naive Bayes classification
- The Laplace estimator
- Applying numeric features with Naive Bayes
2. Mastering Decision Trees
- Divide and conquer strategy
- The C5.0 decision tree algorithm
- Selecting the optimal split
- Pruning decision trees
3. Mastering Neural Networks
- Transition from biological to artificial neurons
- Activation functions
- Network topology
- Layer count
- Direction of information flow
- Node count per layer
- Training neural networks using backpropagation
- Deep Learning
4. Mastering Support Vector Machines
- Classification via hyperplanes
- Identifying the maximum margin
- Linearly separable data scenarios
- Non-linearly separable data scenarios
- Utilizing kernels for non-linear spaces
5. Mastering Clustering
- Clustering as a machine learning objective
- The k-means clustering algorithm
- Using distance metrics for cluster assignment and updates
- Determining the ideal number of clusters
6. Evaluating classification performance
- Handling classification prediction data
- In-depth analysis of confusion matrices
- Utilizing confusion matrices for performance measurement
- Performance metrics beyond accuracy
- The kappa statistic
- Sensitivity and specificity
- Precision and recall
- The F-measure
- Visualizing performance trade-offs
- ROC curves
- Forecasting future performance
- The holdout method
- Cross-validation
- Bootstrap sampling
7. Optimizing standard models for enhanced performance
- Automated parameter tuning with caret
- Building a basic tuned model
- Customizing the tuning workflow
- Enhancing model performance via meta-learning
- Concepts of ensembles
- Bagging
- Boosting
- Random forests
- Training random forests
- Assessing random forest performance
ADDITIONAL TOPICS
8. Classification via Nearest Neighbors
- The kNN algorithm
- Distance calculation
- Selecting an appropriate k
- Data preparation for kNN
- The nature of the kNN algorithm as a lazy learner
9. Classification rules
- Separate and conquer approach
- The One Rule algorithm
- The RIPPER algorithm
- Deriving rules from decision trees
10. Regression
- Simple linear regression
- Ordinary least squares estimation
- Correlations
- Multiple linear regression
11. Regression trees and model trees
- Incorporating regression into tree structures
12. Association rules
- Association rule learning with the Apriori algorithm
- Measuring rule interest via support and confidence
- Constructing 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.
