Course Outline
Introduction
This module offers a foundational overview of machine learning, discussing when to apply it, key considerations, and its implications, including advantages and limitations. It covers data types (structured, unstructured, static, and streamed), data validity and volume, the distinction between data-driven and user-driven analytics, and the comparison between statistical models and machine learning models. Additionally, it addresses the challenges of unsupervised learning, the bias-variance trade-off, iterative evaluation, cross-validation methods, and the paradigms of supervised, unsupervised, and reinforcement learning.
KEY TOPICS
1. Grasping Naive Bayes
- Foundational concepts of Bayesian methods
- Probability theory
- Joint probability
- Conditional probability via Bayes' theorem
- The Naive Bayes algorithm
- Naive Bayes classification
- Laplace smoothing
- Incorporating numerical features into Naive Bayes
2. Mastering Decision Trees
- The divide-and-conquer strategy
- The C5.0 decision tree algorithm
- Selecting optimal splits
- Pruning decision trees
3. Exploring Neural Networks
- Transition from biological to artificial neurons
- Activation functions
- Network architecture
- Determining the number of layers
- Direction of information flow
- Configuring nodes per layer
- Training networks using backpropagation
- Deep Learning fundamentals
4. Comprehending 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. Understanding Clustering
- Clustering as a machine learning task
- The k-means clustering algorithm
- Using distance metrics for cluster assignment and updates
- Determining the optimal number of clusters
6. Assessing Classification Performance
- Working with classification prediction datasets
- Analyzing confusion matrices in detail
- Utilizing confusion matrices for performance measurement
- 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
- Leveraging caret for automated parameter tuning
- Constructing basic tuned models
- Customizing the tuning workflow
- Enhancing performance through meta-learning
- Concepts of ensembles
- Bagging
- Boosting
- Random forests
- Training random forest models
- Evaluating random forest efficacy
SECONDARY TOPICS
8. 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. Classification Rules
- The separate-and-conquer approach
- The One Rule algorithm
- The RIPPER algorithm
- Deriving rules from decision trees
10. Regression Fundamentals
- Simple linear regression
- Ordinary least squares estimation
- Correlation analysis
- Multiple linear regression
11. Regression Trees and Model Trees
- Integrating regression into tree structures
12. Association Rules
- The Apriori algorithm for association rule mining
- Measuring rule significance via support and confidence
- Constructing rule sets using the Apriori principle
Additional Content
- 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.
