Description
Advanced Machine Learning Specialization course provided by the site Coursera It introduces you to the latest artificial intelligence techniques and explains how to program computers to solve industrial problems, play games, see, read, and speak. This collection consists of 7 training courses that will teach you the topics of artificial intelligence comprehensively and in detail.
The first course of this series introduces you to deep learning and working with modern neural networks. The second course teaches you how to win a data science competition and learn the advanced topics of the field. In the third course, you will learn Bayesian methods for machine learning. The fourth course is related to reinforcement learning and the fifth course explains the topics of deep learning in computer vision. The sixth course introduces you to natural language processing and the seventh course solves the LHC challenges with machine learning.
Items taught in this course:
- Deep learning and working with neural networks
- Data science
- Bayesian methods for machine learning
- Reinforcement learning
- Deep learning in computer vision
- Natural Language Processing
- Solving LHC challenges with machine learning
Specifications of the Advanced Machine Learning Specialization course:
- English language
- Duration: 214 hours
- Number of courses:-
- Education level: Intermediate
- teacher : Evgeny Sokolo
- File format: mp4
Course headings
Introduction to optimization
Introduction to neural networks
Deep learning for images
Unsupervised representation learning
Deep learning for sequences
Introduction & Recap
Feature Preprocessing and Generation with Respect to Models
Final Project Description
Exploratory Data Analysis
Metrics Optimization
Hyperparameter Optimization
Competitions go through
Introduction to Bayesian methods & Conjugate priors
Expectation-Maximization algorithm
Variational Inference & Latent Dirichlet Allocation
Markov chain Monte Carlo
Variational Autoencoder
Gaussian processes & Bayesian optimization
Intro: Why should I care?
At the heart of RL: Dynamic Programming
Model-free methods
Approximate Value Based Methods
Policy-based methods
Exploration
Introduction to image processing and computer vision
Convolutional features for visual recognition
Object detection
Object tracking and action recognition
Image segmentation and synthesis
Intro and text classification
Language modeling and sequence tagging
Vector Space Models of Semantics
Sequence to sequence tasks
Dialog systems
Introduction into particle physics for data scientists
Particle identification
Search for New Physics in Rare Decays
Search for Dark Matter Hints with Machine Learning at new CERN experiment
Detector optimization
Course prerequisites
As prerequisites we assume calculus and linear algebra (especially derivatives, matrices and operations with them), probability theory (random variables, distributions, moments), basic programming in python (functions, loops, numpy), basic machine learning (linear models, decision trees, boosting and random forests). Our intended audience are all people who are already familiar with basic machine learning and want to get a hands-on experience of research and development in the field of modern machine learning.
Pictures
Sample video
Installation guide
After extracting, watch with your favorite player.
Subtitle: English and…
Quality: 720p
download link
Password file(s): www.downloadly.ir
Size
10.7 GB
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