Description

This course is an in-depth course on Machine Learning in Python with an introduction to Deep Learning. Any previous knowledge about Machine Learning or Deep Learning is not required.

Course Syllabus

Tooling

  1. Python 3 vs Python 2
  2. Python 3.x Installation
  3. PyCharm – IDE
  4. Executing Python Scripts
  5. pip – Packet Manager
  6. IPython – Interactive Console
  7. Jupyter Notebook
  8. virtualenv – Isolated Python Installations
  9. Tooling Summary
  10. Tooling for Data Science

Data Visualisation with matplotlib

  1. Basic Line Plots
  2. More Series Customization
  3. Log and Symlog Scale
  4. Multiple Plots
  5. Interactive Plots

Python Crash Course (optional)

  1. Data Types
  2. Functions
  3. Useful Builtin Functions

Data Processing with Pandas

  1. Importing and Exporting Data
  2. Basic Transformations
  3. Aggregation
  4. Filtering
  5. Split-Apply-Combine Pattern
  6. Rolling
  7. Processing Missing Values

Introduction to Machine Learning

  1. What is Machine Learning?
  2. Basic Concepts
  3. Problem Types
  4. Basic Questions
  5. Common Workflow
  6. Algorithm Cheat-Sheet
  7. Supervised Learning
  8. Bias-Variance Trade Off
  9. Case Study: Iris Classification

Regression Linear Models

  1. Simple Linear Regression
  2. Multiple Linear Regression
  3. Ridge Regularisation
  4. Lasso Regularisation
  5. ElasticNet Regularisation

Feature Engineering & Selection

  1. Pipelines
  2. One Hot Encoding
  3. Polynominal & Interaction Terms
  4. ln(x+1) Transformation
  5. Feature Selection

Cross Validation and Grid Search

  1. Cross Validation
  2. Cross Validation Strategies
  3. Grid Search

Classification

  1. Logistic Regression
  2. Binary Classification
  3. Multiclass Classification
  4. Evaluation for Model Selection

Business Applications

Models

  1. k-Nearest Neighbors
  2. Linear & Logistic Regression, Lasso, Ridge and ElasticNet Regularization Recap
  3. Neural Networks
  4. Support Vector Machines
  5. Kernelized Support Vector Machines
  6. Decision Trees
  7. Random Forests and Boosting
  8. Classificators Comparison

Clustering

  1. k-Means Clustering
  2. Agglomerative Clustering
  3. Hierarchical Clustering and Dendograms
  4. DBSCAN
  5. Evaluating Clustering with Ground Truth
  6. Comparing Clustering on Digits

Dimensionality Reduction

  1. Principal Component Analysis (PCA)
  2. Non-negative matrix factorisation (NMA)
  3. Decomposing Signals with NMF
  4. Manifold Learning with t-SNE

Recommendation Systems

  1. Introduction to Recommendation Systems
  2. Suprise Library
  3. CI&T Deskdrop Dataset
  4. Cold Start
  5. Building Model and Evaluation
  6. Popularity Model
  7. Content-Based Filtering
  8. Collaborative Filtering
  9. Testing Models

BigData with dask

  1. What is dask?
  2. dask as a Task Scheduler
  3. Working on a Computational Cluster
  4. DataFrame
  5. Dask-ML
  6. Dask Alternatives

Intro to Deep Learning

  1. Installation and Tooling Overview
  2. Neutral Network Introduction
  3. Keras Introduction
  4. Feedforward Neural Network
  5. Image Classification
  6. Convolutional Neural Network
  7. Image Classification
  8. Activation Function
  9. Learning Process
  10. Backpropagation
  11. Recurrent Neural Networks with LSTM
  12. Averaging with LSTM
  13. Text Generation with LSTM
  14. Neural Network Architectures
  15. GoogLeNet
  16. Self-Taught Learning
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