Description
Introducing Python for Machine Learning & Deep Learning in One Semester course
- Course introduction
- An introduction to machine learning and deep learning
- Introduction to Google Colab
- Python crash course
- Data preprocessing
Supervised machine learning
- Regression analysis
- logistic regression
- K-Nearest Neighbor (KNN)
- Bayes theorem and simple Bayes classifier
- Support Vector Machine (SVM)
- Decision trees
- Random forest
- Reinforcement methods in machine learning
- An introduction to neural networks and deep learning
- Activation functions
- Loss functions
- Release back
- Neural networks for regression analysis
- Neural networks for classification
- Deletion regularization and batch normalization
- Convolutional Neural Network (CNN)
- Recurrent Neural Network (RNN)
- Auto encoders
- Generative Adversarial Network (GAN)
Unsupervised machine learning
- K-Means Clustering
- Hierarchical clustering
- Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
- Gaussian mixture model (GMM) clustering.
- Principal Component Analysis (PCA)
What you will learn in the Python for Machine Learning & Deep Learning in One Semester course
- Theory, mathematics and implementation of machine learning and deep learning algorithms.
- Regression analysis.
- Classification models used in classical machine learning such as logistic regression, KNN, support vector machines, decision trees, random forest and reinforcement methods in machine learning.
- Build artificial neural networks and use them for regression and classification problems.
- Using GPU with deep learning models
- Convolutional neural networks
- Transfer learning
- Recurrent Neural Networks
- Prediction and classification of time series
- Auto encoders
- Hostile productive networks
- Python from scratch
- Numpy, Matplotlib, seaborn, Pandas, Pytorch, scikit-learn and other Python libraries.
- More than 80 projects have been solved with machine learning and deep learning models.
This course is suitable for people who
- Machine learning and deep learning course students
- Beginners who want to learn machine learning and deep learning from scratch
- Artificial intelligence researchers
- Students and researchers who want to develop Python programming skills to solve machine learning and deep learning tasks.
- Those who know MATLAB and other programming languages and want to switch to Python for machine learning and deep learning.
Details of Python for Machine Learning & Deep Learning in One Semester course
- Publisher: Yudmi
- teacher: Zeeshan Ahmad
- Training level: beginner to advanced
- Training duration: 46 hours and 45 minutes
- Number of courses: 305
Python for Machine Learning & Deep Learning course topics in One Semester
Prerequisites of Python for Machine Learning & Deep Learning in One Semester course
- Some programming knowledge is preferable but not necessary
- Gmail account (For Google Colab)
Course images
Sample video of the course
Installation guide
After Extract, view with your favorite Player.
Subtitle: None
Quality: 720p
download link
File(s) password: www.downloadly.ir
Volume
15.3 GB
Be the first to comment