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 Complete Machine Learning with Python B1589 QR Code
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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.

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