AI & ML with Python

Dates: 5-April-19 till 20-April-19

Academic Director: I. Nikolakopoulos (in)

Tech Instructor: T. Tagaris 

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This is a certified program by Athens Tech College, the first educational institution in Greece that specialises in computer science and ICT studies.  

Participant’s registration (payment’s completion) implies full compliance and acceptance οf  Code.Learn – Terms & Conditions.



  • This specific Code.Learn program lasts 3 weeks (3 Fridays, 3 Saturdays) with 35 hours of lectures and hands-on exercise on a real life project.

Key Objectives – Curriculum (High Level):

The core perspectives of this program will be to present, explore and adequately cover with extended real-life business case studies & industry scenarios the following thematic units:

Day 1

  • Python & Numpy

Day 2

  • Data analysis with Pandas
  • Data visualization with matplotlib
  • Hands-on example (data analysis)
  • Machine Learning
  • Assignment 1 (Data Analysis)

Day 3

  • Popular ML algorithms
  • Supervised:
    • k Nearest Neighbors
    • Naive Bayes
    • Decision Trees
    • Neural Networks
  • Unsupervised:
    • k means.
    • Hierarchical clustering.
  • Intro to scikit-learn:
    • Library structure
    • Defining and training models.
    • Evaluating the results
    • Hyperparameter optimization

Day 4

  • Advanced ML concepts
    • Bias/variance tradeoff
    • Regularization
    • Typical holdout strategy
    • Cross-validation
    • Handling missing data
    • Feature encoding
    • Feature scaling
    • Curse of dimensionality
    • Feature selection
    • Feature extraction
    • Class imbalance
    • Confusion matrix metrics
    • Over/Under sampling
  • Implementing advanced pre-processing/evaluation techniques in python. Complete supervised ML workflow
  • Hands-on example (Classification)
  • Assignment 2 (Classification)

Day 5

  • Natural Language Processing
    • Intro to unstructured data
    • String operations
    • Tokenization
    • Stopword removal
    • Stemming/Lemmatization
    • Vector Space Models
      • The Bag-of-words model
      • TF-IDF
  • Hands-on example (text classification):
    • Load a text dataset.
    • Process the documents through NLP techniques
    • Convert it to a structured ML problem (tf-idf)
    • Train and evaluate a classification model
    • Examine the impact of the most important parameters to the result

Day 6

  • Clustering
    • – Intro to clustering
    • – Step-by-step k-means
    • – Clusteing solution evaluation
    • Clustering in scikit-learn
  • Hands-on example (text clustering)
    • Load a Movie recommendation dataset
    • Process it using MLP techniques
    • Convert ti to a structured ML problem
    • Train a clustring model
    • Use evaluation metrics to optimize the above model
    • Empirically evaluate solution
  • Closing remarks
    • General tips for ML
    • Tips for clustering
    • Tips for NLP
    • How to approach a real life ML problem
    • Real life applications and ideas
    • Questions/Discussion
  • Work in class
    • Assignment 3 (Reuters dataset clustering)

Target Audience

Data analysts, data scientists, data engineers, BI engineers/developers, information-visualization-analytics professionals as well as computer scientists, software engineers, developers and consultants who vision their future in data-relevant career paths are welcome to participate to this code.learn program and unlock the full potentiality of the topics taught by upskilling their  future career.

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Additional information

Certification Institute:

Athens Tech College

Save the Dates

– 3 Fridays:
5/4, 12/4, 19/4: 18.00-21.15
– 3 Saturdays:
6/4, 13/4, 20/4: 10.00-18.00


5-April-19 till 20-April-19
35 hours


Athens Tech College

Registration Policy

Early Bird: 450€ (Limited number of tickets available)

Normal Registration: 500€ (Limited number of tickets available)

Participant’s registration (payment's completion) implies full compliance and acceptance οf Code.Learn – Terms & Conditions.