Machine Learning by using Python :
With brand new sections as well as updated and improved content, you get everything you need to master Machine Learning in one course! The machine learning field is constantly evolving, and we want to make sure students have the most up-to-date information and practices available to them:
Brand new sections include:
- Foundations of Deep Learning differentiate between machine and deep learning, the building blocks of neural networks, descriptions of tensor and tensor operations, categories of machine learning and advanced concepts such as over- and underfitting, regularization, dropout, validation and testing and much more.
- Computer Vision in the form of Convolutional Neural Networks covering building the layers, understanding filters / kernels, to advanced topics such as transfer learning, and feature extractions.
And the following sections have all been improved and added to:
- All the codes have been updated to work with Python 3.6 and 3.7
- The codes have been refactored to work with Google Colab
- Deep Learning and NLP
- Binary and multi-class classifications with deep learning.
Inside the course, you’ll learn how to:
- Gain complete machine learning tool sets to tackle most real world problems
- Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.
- Combine multiple models with by bagging, boosting or stacking
- Make use to unsupervised (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data
- Develop in Jupyter (IPython) notebook, Spyder and various IDE
- Communicate visually and effectively with Matplotlib and Seaborn
- Engineer new features to improve algorithm predictions
- Make use of train/test, K-fold and Stratified K-fold cross validation to select correct model and predict model perform with unseen data
- Use SVM for handwriting recognition, and classification problems in general
- Use decision trees to predict staff attrition
- Apply the association rule to retail shopping datasets
- And much much more!
Who this course is for:
- Any one who has a deep interest in the practical application of ML to real world problems
- moveing beyond the basics and develop an understanding of the whole range of ML algorithms
- Any intermediate to advanced EXCEL users who is unable to work with large datasets
- Anyone interested to present their findings in a professional and convincing manner
- Starting or transit into a career as a data scientist
- Basic Python programming knowledge is necessary
- Good understanding of linear algebra
Build Powerful Machine Learning Models to Solve Any Problem
You’ll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.
Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing.
The term artificial intelligence was coined in 1956, but AI has become more popular today thanks to increased data volumes, advanced algorithms, and improvements in computing power and storage.
This early work paved the way for the automation and formal reasoning that we see in computers today, including decision support systems and smart search systems that can be designed to complement and augment human abilities.
Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. EED Academy-Best online Learning Free and Paid Courses
After Complete this course go to Part 2