Data Visualization, Machine Learning, and Natural Language Processing with Python and KNIME

Key Outcomes

The bootcamp’s significance lies in its focus on providing learners with practical skills in data visualization using Python, machine learning model representation, regression analysis, clustering, and recommender systems. It aims to equip learners with skills to create visualizations that present data insights effectively, leading to better decision-making. Additionally, learners will gain expertise in applying machine learning algorithms, such as decision tree classifiers, ensemble models, regression trees, and clustering techniques, to solve real-world problems. This course is particularly useful for learners in data science, data analysis, and data engineering, seeking to enhance their knowledge and practical skills in data visualization, machine learning, and natural language processing with Python and KNIME.

Program Modules

This bootcamp comprises six modules, starting with basic data visualization with Python and progressing to interactive dashboards with Python, supervised machine learning, unsupervised machine learning, and natural language processing with Python.


  • Week 1- Basic Data Visualization with Python
    Introduction to Data Visualization
    Python and Jupyter Notebooks / Visual Code
    Plotting with Matplotlib
    Line Plots, Area Plots, Histograms, Bar Charts, Pie Charts
    Outlier Detection with Box Plots
    Correlation Analysis with Scatter Plots
    Data Visualization using Seaborn
    Charts and Heat Maps
    Quiz: Data Visualization
    Project: Data Visualization implementation
  • Week 2- Interactive Dashboards with Python
    Dashboards with Plotly and Dash, and Bokeh
    Introduction to Dashboarding
    Introduction to Plotly
    Introduction to Dash
    Introduction to Bokeh
    Interactive Plots and Dashboards with complex and streaming data
    Interactive network graph with pyvis
    Machine Learning Models Visualization and deployment with StreamLit
    Quiz: Interactive Dashboards
    Project: Dashboard implementation
  • Week 3 - Supervised Machine Learning: Classification
    What is Machine Learning?
    Machine Learning Applications/Examples
    Typical Machine Learning Tasks and Pipeline
    Supervised Vs. Unsupervised Machine Learning
    Getting familiar with machine learning Python libraries (SciKit learn)
    Code-free Machine Learning Model Representation
    Introduction to KNIME Analytics Platform
    Downloading and Installing KNIME
    Creating new Workflows in KNIME
    Different Types of Features / Variables
    Feature Engineering (Transformation); Data Preprocessing
    Handling Missing data
    Feature Scaling and Normalization
    Handling Outliers
    One hot encoding
    Decision Tree Classifier
    Classification Model Evaluation
    Evaluation Metrics; Accuracy, Precision, Recall, and ROC Curve
    Ensemble Models
    Bootstrapping, Bagging, Boosting, Random Forest, and Stacking
    k-Nearest Neighbor Classifier
    Naïve Bayes Classifier
    Quiz: Classification
    Project: Kaggle Challenge to implement Classification Algorithms
  • Week 4 – Supervised Machine Learning: Regression
    What is Regression Analysis? (Linear Regression)
    Multiple Linear Regression
    Regression Model Evaluation
    Feature Engineering (Selection); Forward/Backward/Stepwise Regression
    Feature Engineering (Creation); Polynomial Regression
    Regression Trees and Ensemble Regression
    Feature Engineering (Extraction); Principal Component Analysis
    Quiz: Regression
    Project: Kaggle Challenge to implement Regression Algorithms
  • Week 5 – Unsupervised Machine Learning: Clustering and Recommender Systems
    What is clustering?
    K-means Clustering
    Metrics for Evaluating Clustering
    Introduction to Recommender Systems
    Evaluation of Recommender Systems
    Collaborative filtering
    Content-based filtering
    Quiz: Unsupervised Learning
    Project: Recommender System Implementation
  • Week 6 – Natural Language Processing with Python
    Introduction to NLP
    NLP Applications/Examples
    Natural language processing tasks with TextBlob, NLTK, and spacy
    Part-of-Speech Tagging, Bag of Words, Term Frequency, Inverse Document Frequency (TF-IDF),
    Unigram, Bigram, and Trigrams and their use in Sentiment Analysis
    Sentiment Analysis with Logistic Regression and Naïve Bayes
    Knowledge Graphs from Text
    Quiz: Natural Language Processing
    Project: Sentiment Analysis Implementation

Program Experience

Office Hours with Learning Facilitators


Coding Exercises in Each Module

Bite-Sized Learning

Knowledge Checks

Dedicated Program Support Team

Mobile Learning App

Peer Discussion

Capstone Project

Bonus Content on Advanced Topics

Career Support

Certification & Digital Badge

Who Should Attend?

This online program is designed for anyone who is interested in acquiring programming skills in Python & Machine Learning. No prior programming knowledge is required.


Upon successful completion of the program, participants will receive a digital certificate of completion from American Institute for IT Professionals. This is a training program, and it is not eligible for academic credit.

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