Rating 0 out of 5 (0 ratings in Udemy)
What you'll learn- Use Python for Machine Learning
- Learn from the Theoretical concepts details to the Practical Coding Implementation
- Building Apps that can make prediction using various Machine Learning Models
- Implement Machine Learning Algorithms KNN, SVM, Decision Tree, NB, Random Forest, etc...
- Data Clustering usnig K-Means and Hierarchical Clustering
- Python Libraries : Numpy, Pandas, Scikit-learn, Matplotlib
- Learn How Handle Textual, Images, …
Rating 0 out of 5 (0 ratings in Udemy)
What you'll learn- Use Python for Machine Learning
- Learn from the Theoretical concepts details to the Practical Coding Implementation
- Building Apps that can make prediction using various Machine Learning Models
- Implement Machine Learning Algorithms KNN, SVM, Decision Tree, NB, Random Forest, etc...
- Data Clustering usnig K-Means and Hierarchical Clustering
- Python Libraries : Numpy, Pandas, Scikit-learn, Matplotlib
- Learn How Handle Textual, Images, Videos Data with Python
- Machine Learning Concepts (Confusion matrix, Evaluation Metrics, etc...)
- Tensorflow and Keras with practical Projects and examples
- Resampling, Dimentionalty Reduction, Data Scalling, Regression, Regularization, Over-fitting and Under-fitting
- Deep Learning and deep neural nets || from the basics of Neural Nets to LSTM and CONVNETS
- NLP and Practical Textual data preprocessing (Noise Removal, Normalization, Tokenization, etc...) using NLTK
- Transfer Learning Approach
- Solve Real world problems using Machine Learning
- Build Real world Projects with many Approaches and Algorithms
- Know which Machine Learning Algorithm to use for which problem
DescriptionWelcome to Python Machine Learning course where I believe, it will answer your questions about ML.
For me this course is built around Three things :
Learning and Understanding the Difficult of this field with the Easy Way.
Learning from the theoretical concepts to Python practical examples.
Building a learning relation between you and I where,You will get Learning, Help and Guidance from my side.
As you know, Machine Learning (ML) and Artificial Intelligence (AI) is what the world talk about this days;
So If you want to :
Get involve in this field and I advise you to;
Know how to build and test Machine Learning Models;
Know how to solve real world problems;
Expand your knowledge in the Python Programming Language besides what you will learn in the course road-map.
Then :
From the core concepts of ML, Natural Language Processing, Deep Learning and Neural Nets moving to Real World Problems Solving and projects construction:
With more than 15 presentations cover the Theoretical Concepts built with simplest ideas that let you learn in the fast easy way.
With more than 10 practical Python coding examples.
With more than 12 Projects of Real World Problems Solved with Step by Step Detailed Building Guide.
This course will guide you with a step by step learning process :
Starting with various presentations as an introduction to fields of AI ML & DL and the Intersection between them.
Moving to concepts, methods and approaches belong to ML such as Cross Validation, Resampling, Dimensionality Reduction and Natural Language Processing.
As well as, we will talk about Regression, Regularization and Over/Under Fitting in detail with practical Python examples
Next, we will jump to the Projects Building part where we solve Real World Problem in the ML way.
Various projects that cover various problems in many different fields : Health Care, Fashion, Computer Vision to Speech and Emotion Recognition and Sentiment Analysis with the diverse data form Textual, Numerical, Audio files and Images.
Different ML Algorithms are used and appliedeither in the course learning process or in the project building :
SVM, NB, Random Forest, KNN, K-Means, SGDClassifier, Decision Tree, etc...
Simple/Multiple Linear, Logistic and L1, L2 Regression.
PCA and Autoencoders for Dimentionalty Reduction.
From Simple Neural Nets, Multilayer Perceptron to Deep Convolutional Nets and LSTM.
Applying Transfer Learning Approach.
NLP : RegExp, Stemming/Lemming, POS-tag, Tokenization, Noise Removing, TF-IDF.
Computer Vision
