Coursera – Advanced Machine Learning Specialization (7 Courses) 2020-6

Description

Advanced Machine Learning Specialization is a course offered by the Coursera site that introduces you to the latest AI techniques and explains how to program a computer to solve industrial problems, run games, see, read, and talk. . This set consists of 7 training courses that 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 advanced topics. 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 by machine learning.

Items that will be 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

Solve LHC challenges with machine learning

Advanced Machine Learning Specialization course specifications

English language

Duration: 214 hours

Number of courses: -

Level of education: Intermediate

Instructor: Evgeny Sokolo

File format: mp4

Topics

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

Prerequisite

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 hand-on experience of research and development in the field of modern machine learning.

Installation

After Extract, watch with your favorite Player.

Subtitle: English and.

Quality: 720p