Description
Introduction to Python for Machine Learning and Deep Learning in a 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
- let go again
- Neural networks for regression analysis
- Neural networks for classification
- Deletion regularization and batch normalization
- Convolutional Neural Network (CNN)
- Recurrent neural network (RNN)
- Auto encoder
- Generative Adversarial Network (GAN)
Unsupervised machine learning
- K-Means Clustering
- hierarchical clustering
- Density-based spatial clustering of noisy applications (DBSCAN)
- Gaussian mixture model (GMM) clustering.
- Principal Component Analysis (PCA)
What you will learn in the Python for Machine Learning and 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, ANN, support vector machines, decision trees, random forest, and reinforcement learning methods.
- Build artificial neural networks and use them for regression and classification problems.
- Using a GPU with deep learning models
- Convolutional neural networks
- Transfer learning
- Recurrent neural networks
- Prediction and classification of time series
- Auto encoder
- Hostile productive networks
- Python from scratch
- Numpy, Matplotlib, Seaborn, Pandas, Pytorch, Scikit-Learn and other Python libraries.
- More than 80 projects were solved using machine learning and deep learning models.
This course is suitable for people who
- Students of the courses “Machine Learning” and “Deep Learning”.
- Beginners who want to learn machine learning and deep learning from scratch
- Artificial intelligence researcher
- 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 about the course “Python for Machine Learning and Deep Learning in One Semester”.
- Editor: Udemy
- Lecturer: Zeeshan Ahmed
- Training level: beginner to advanced
- Training duration: 46 hours and 45 minutes
- Number of courses: 305
Course topics “Python for Machine Learning and Deep Learning” in one semester
Prerequisites for Python for Machine Learning and Deep Learning in a Semester Course
- Some programming knowledge is desirable but not necessary
- Gmail account (for Google Colab)
Course pictures
Sample video of the course
installation Guide
After extracting, you can watch it with your favorite player.
Subtitles: None
Quality: 720p
Download link
File(s) password: www.downloadly.ir
File size
15.3GB