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

  1. 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
  2. Data Visualisation with matplotlib:
    1. Basic Line Plots
    2. More Series Customization
    3. Log and Symlog Scale
    4. Multiple Plots
    5. Interactive Plots
  3. Python Crash Course:
    1. Data Types
    2. Functions
    3. Useful Builtin Functions
  4. 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
  5. 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
  6. Regression Linear Models:
    1. Simple Linear Regression
    2. Multiple Linear Regression
    3. Ridge Regularisation
    4. Lasso Regularisation
    5. ElasticNet Regularisation
  7. Feature Engineering & Selection:
    1. Pipelines
    2. One Hot Encoding
    3. Polynominal & Interaction Terms
    4. ln(x+1) Transformation
    5. Feature Selection
  8. Cross Validation and Grid Search:
    1. Cross Validation
    2. Cross Validation Strategies
    3. Grid Search
  9. Classification:
    1. Logistic Regression
    2. Binary Classification
    3. Multiclass Classification
    4. Evaluation for Model Selection
  10. Business Applications
  11. Models:
    1. k-Nearest Neighbors
    2. Linear & Logistic Regression
    3. Lasso, Ridge and ElasticNet Regularization Recap
    4. Neural Networks
    5. Support Vector Machines
    6. Kernelized Support Vector Machines
    7. Decision Trees
    8. Random Forests and Boosting
    9. Classificators Comparison
  12. 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
  13. Dimensionality Reduction:
    1. Principal Component Analysis (PCA)
    2. Non-negative matrix factorisation (NMA)
    3. Decomposing Signals with NMF
    4. Manifold Learning with t-SNE
  14. 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
  15. 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
  16. 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