Course Outline
Introduction
This section offers an overview of the appropriate contexts for employing 'machine learning', outlining key considerations, including its advantages and disadvantages. It covers data types (structured/unstructured/static/streamed), data validity and volume, the distinction between data-driven and user-driven analytics, the comparison between statistical and machine learning models, the challenges of unsupervised learning, the bias-variance trade-off, iteration and evaluation processes, cross-validation methods, and the paradigms of supervised, unsupervised, and reinforcement learning.
MAJOR TOPICS
1. Explaining Naive Bayes
- Core principles of Bayesian methods
- Probability theory
- Joint probability
- Conditional probability and Bayes' theorem
- The Naive Bayes algorithm
- Naive Bayes classification
- The Laplace estimator
- Incorporating numeric features with Naive Bayes
2. Explaining Decision Trees
- The divide-and-conquer strategy
- The C5.0 decision tree algorithm
- Selecting optimal splits
- Pruning decision trees
3. Explaining Neural Networks
- Transitioning from biological to artificial neurons
- Activation functions
- Network architecture
- Determining the number of layers
- Information flow direction
- Configuring node counts per layer
- Training networks via backpropagation
- Deep Learning
4. Explaining Support Vector Machines
- Classification using hyperplanes
- Maximizing the margin
- Handling linearly separable data
- Handling non-linearly separable data
- Applying kernels for non-linear spaces
5. Explaining Clustering
- Clustering as a machine learning objective
- The k-means clustering algorithm
- Assigning and updating clusters using distance metrics
- Selecting the optimal number of clusters
6. Assessing classification performance
- Processing classification prediction data
- Analyzing confusion matrices
- Evaluating performance using 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 improved performance
- Automated parameter tuning with caret
- Constructing a basic tuned model
- Customizing the tuning workflow
- Enhancing model performance via meta-learning
- The concept of ensembles
- Bagging
- Boosting
- Random forests
- Training random forest models
- Evaluating random forest performance
MINOR TOPICS
8. Classification via the 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 approach
- The One Rule algorithm
- The RIPPER algorithm
- Deriving rules from decision trees
10. Understanding regression
- Simple linear regression
- Ordinary least squares estimation
- Correlation analysis
- Multiple linear regression
11. Regression and model trees
- Integrating regression into tree structures
12. Understanding association rules
- The Apriori algorithm for association rule mining
- Measuring rule significance via support and confidence
- Constructing rule sets using the Apriori principle
Extras
- Spark/PySpark/MLlib and Multi-armed bandits
Requirements
Proficiency in 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.
