What you’ll learn
-
Uso de Python para Data Science e Machine Learning
-
Visualização profissional de dados com Matplotlib
-
Aprender sobre visualização de dados intuitiva com o Plotly
-
Aprender a usar o Seaborn para análise estatística dos dados
-
Uso do Pandas para análise de dados
-
Uso do Numpy para operações vetoriais e matriciais
-
Regressão Logística
-
K-Nearest-Neighbors (KNNs)
-
K-means-clustering
-
Support-Vector-Machines (SVM)
-
Neural-Networks (Redes Neurais)
-
Uso do Spark para Big-Data
-
Processamento natural de linguagem e sistemas de filto de spam
-
Sistemas de recomendação (usados no NetFlix, Spotify, etc)…
-
Projetos com dados reais
Who this course is for:
- Este público é voltado para quem deseja aprender mais sobre Machine Learning e a principal linguagem de programação voltada para análise de dados: Python,.
Can I download Python para Data Science e Machine Learning – COMPLETO course?
You can download videos for offline viewing in the Android/iOS app. When course instructors enable the downloading feature for lectures of the course, then it can be downloaded for offline viewing on a desktop.Can I get a certificate after completing the course?
Yes, upon successful completion of the course, learners will get the course e-Certification from the course provider. The Python para Data Science e Machine Learning – COMPLETO course certification is a proof that you completed and passed the course. You can download it, attach it to your resume, share it through social media.Are there any other coupons available for this course?
You can check out for more Udemy coupons @ www.coursecouponclub.com
Note: 100% OFF Udemy coupon codes are valid for maximum 3 days only. Look for "ENROLL NOW" button at the end of the post.
Disclosure: This post may contain affiliate links and we may get small commission if you make a purchase. Read more about Affiliate disclosure here.
Disclosure: This post may contain affiliate links and we may get small commission if you make a purchase. Read more about Affiliate disclosure here.
