Course Outline
Introduction
This section offers a comprehensive overview of when to apply 'machine learning', the factors to consider, and its implications, including advantages and disadvantages. It covers datatypes (structured/unstructured/static/streamed), data validity/volume, data-driven vs. user-driven analytics, statistical models vs. machine learning models, challenges of unsupervised learning, the bias-variance trade-off, iteration/evaluation, cross-validation methods, and the distinctions between supervised/unsupervised/reinforcement learning.
MAJOR TOPICS
1. Understanding naive Bayes
- Fundamental concepts of Bayesian methods
- Probability
- Joint probability
- Conditional probability using Bayes' theorem
- The naive Bayes algorithm
- Classification with naive Bayes
- The Laplace estimator
- Application of numeric features with naive Bayes
2. Understanding decision trees
- The divide-and-conquer strategy
- The C5.0 decision tree algorithm
- Selecting the optimal split
- Pruning decision trees
3. Understanding neural networks
- Transition from biological to artificial neurons
- Activation functions
- Network topology
- Determining the number of layers
- The direction of information flow
- Defining the number of nodes per layer
- Training neural networks via backpropagation
- Deep Learning
4. Understanding Support Vector Machines
- Classification using hyperplanes
- Identifying the maximum margin
- Scenarios with linearly separable data
- Scenarios with non-linearly separable data
- Utilizing kernels for non-linear spaces
5. Understanding clustering
- Clustering as a machine learning task
- The k-means algorithm for clustering
- Assigning and updating clusters using distance metrics
- Selecting the optimal number of clusters
6. Measuring performance for classification
- Working with classification prediction data
- An in-depth look at confusion matrices
- Utilizing confusion matrices to assess performance
- 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. Tuning standard models for improved performance
- Using caret for automated parameter tuning
- Building a simple tuned model
- Customizing the tuning process
- Enhancing model performance through meta-learning
- Understanding ensembles
- Bagging
- Boosting
- Random forests
- Training random forests
- Evaluating random forest performance
MINOR TOPICS
8. Understanding classification using the nearest neighbors
- The kNN algorithm
- Calculating distance
- Selecting an appropriate k
- Preparing data for kNN implementation
- Why the kNN algorithm is considered 'lazy'
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
- Correlations
- Multiple linear regression
11. Understanding regression trees and model trees
- Incorporating regression into trees
12. Understanding association rules
- The Apriori algorithm for association rule learning
- Measuring rule interest via support and confidence
- Constructing a rule set using the Apriori principle
Extras
- Spark/PySpark/MLlib and Multi-armed bandits
Requirements
Python Proficiency
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.
