The Data Science Developer Bootcamp is a 165-hour on-site program aimed at professionals with basic coding knowledge that seek to learn the main Artificial Intelligence techniques and how to apply them into different industry cases.
This program starts with the preparation of data (analysis and cleaning) using Python and R. Then students will learn how to find meaningful information by navigating through different methodologies such as historic human labels, clusters, segmentations and patterns. Finally, deep learning will be applied to tackle more complex data followed by data visualization. The course will finish by analyzing the complete cycle of Artificial Intelligence (AI) and the wide range of the commercial applications
Powered by IMMUNE Institute, this bootcamp combines a faculty that includes top IT professionals with learning by doing methodology to provide real-world scenarios and a practical approach
1. INTRODUCTION TO AI. KEY MATHEMATICAL CONCEPTS FOR AI:
· Introduction and key mathematical concepts for AI
· Linear Algebra
· Probability and Statistic for Machine Learning
2. DATA MANIPULATION AND ANALYSIS (PYTHON/R).
· Data manipulation and Analysis databases, sql, no sql, apis, webservices, scripting..)
· Data preprocessing (cleaning and manipulation). Main libraries in Python y R (panda, numpy)
· Exploratory analysis (correlation, variance analysis, descriptive statistics..)
3. SUPERVISED MACHINE LEARNING. CLASSIFICATION AND REGRESSION
· Probabilistic model (Naive Bayes & Logistic Regression). K-NN , Decision trees & Random forest SVM
· Linear and non-linear regression.
· Resampling Methods, cross validation.
· Overfitting & Bias.
4. UNSUPERVISED MACHINE LEARNING.
· Dimensionality reduction (PCA, SVD...)
· Latent variables modeling
· Clustering (kmeans, hierarchical, DBSCAN...)
· Anomalies detection
5. DEEP LEARNING AND NEURAL NETWORKS
· Deep Learning introduction. How to build a neural network.
· Densely connected network.
· Convolutional neural network (CNN).
· Recurrent neural network (RNN).
· Reinforcement learning.
6. DATA VISUALIZATION.
· Key visualization concepts.
· More relevant libraries in Python & R: (matplotlib, ggplot2, plotly, seaborn, bokeh).
· Using graphics in D3 with nvd3 from Python.
· Grafos using Phyton NetworkX and PySpark GraphFrames
· Key open source and commercial tools.
7. AI LIFECYCLE & AI COMMERCIAL TOOLS
· AI lifecycle
· Biases, regulation and future trends
· AI commercial frameworks (IBM , BIGML…)