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.
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.
Data Scientists.
Machine Learning Engineers.
Data Analysts.
Python Programmers interested in machine learning.
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.
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).
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.
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.
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.
