Complete Machine Learning with Python

Overview:

Introduction:

This training program provides participants with essential knowledge and skills in machine learning using Python. It empowers them to understand and implement machine learning algorithms and techniques to solve real-world problems.

Program Objectives:

At the end of this program, participants will be able to:

• Understand the basics of machine learning and its applications.

• Utilize Python libraries for data analysis and machine learning.

• Implement various machine learning algorithms.

• Evaluate and improve the performance of machine learning models.

• Apply machine learning techniques to real-world datasets.

Targeted Audience:

• Data Scientists.

• Machine Learning Engineers.

• Data Analysts.

• Python Programmers interested in machine learning.

Program Outline:

Unit 1:

Introduction to Machine Learning and Python:

• Overview of machine learning concepts and types.

• Introduction to Python for machine learning.

• Setting up the Python environment (Anaconda, Jupyter Notebook).

• Exploring essential Python libraries: NumPy, Pandas, Matplotlib, and Seaborn.

• Understanding data preprocessing and cleaning techniques.

Unit 2:

Supervised Learning Algorithms:

• Introduction to supervised learning and its applications.

• Implementing linear regression and logistic regression.

• Understanding decision trees and random forests.

• Exploring support vector machines (SVM).

• Evaluating model performance with metrics (accuracy, precision, recall, F1 score).

Unit 3:

Unsupervised Learning Algorithms:

• Introduction to unsupervised learning and its applications.

• Implementing k-means clustering and hierarchical clustering.

• Understanding principal component analysis (PCA).

• Exploring anomaly detection techniques.

• Evaluating clustering performance and visualization techniques.

Unit 4:

Advanced Machine Learning Techniques:

• Introduction to ensemble methods (bagging, boosting).

• Implementing gradient boosting and XGBoost.

• Understanding neural networks and deep learning basics.

• Utilizing TensorFlow and Keras for deep learning models.

• Exploring natural language processing (NLP) with Python.

Unit 5:

Model Evaluation and Optimization:

• Understanding overfitting and underfitting.

• Implementing cross-validation techniques.

• Exploring hyperparameter tuning (Grid Search, Random Search).

• Using feature selection and engineering techniques.

• Applying machine learning models to real-world datasets and case studies.