Introduction To Machine Learning With Python Course (FREE)

Course Instructor:

Luca Arrotta is Ph.D. student at the Department of Computer Science of the University of Milan. His interests are Machine Learning, Data Analysis, IoT, Mobile Programming, and Indoor Positioning. His research currently focuses on Pervasive Computing, Context-awareness, Explainable AI, and Human Activity Recognition in smart environments.

Course Overview & Lectures

Duration: 1.5 hours


Lecture 1: Course Introduction

Python Libraries

Lecture 2: Machine Learning Introduction

(Machine Learning (ML) Introduction: -AI, Machine Learning, Deep Learning -Supervised/unsupervised with subcategories -Reinforcement Learning and Semi-supervised (brief introduction) -ML pipeline -Overfitting/Underfitting)

Lecture 3: Machine Learning Libraries

(Numpy: array operations, matrix operations Pandas: csv and dataframes management and analysis Visualization Tools: matplotlib and seaborn Other libraries: SciPy, Scikit-Learn, Keras)

Lecture 4: Github Notebook-1

Machine Learning Pipeline

Lecture 5: Machine Learning Pipeline

(Get or collect data, Data pre-processing (cleaning, normalization, standardization, dimensionality reduction), Feature extraction and selection (with Deep Learning?), Model selection (development, grid search tuning), Model evaluation (K-fold, confusion matrix etc.)

Lecture 6: Lab ML Pipeline

Lecture 7: Github Notebook-2