American Institute for IT Professionals

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.

Modules

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

Discretization

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

Demonstrations

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.

Certificate

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.