What you'll learn

Mastering Data Science fundamentals
Mastering Machine Learning Fundamentals
How and when to use each Machine Learning model
Make regression using Linear Regression, SVM, Decsision Trees and Ensemble Modeling
Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA

Requirements

Just some high school mathematics level.

Description

Data Science and Machine learning is not just another buzzword. So many professionals who work in different areas such as IT, security, marketing, automation, and even medicine, know that machine learning is the key to development. Without it, so many amazing things that make our lives easier - such as spam-filtering, Google search, relevant ads, accurate weather forecasting or sport prediction - would be impossible. This course is the starting point you've been waiting for.

This course is designed for students and learners who want to demystify the concepts, statistics, and math behind machine learning algorithms, and who are curious to solve real-world problems using machine learning. The course is structured to start with the basics, and then to gradually develop an understanding of the array of machine learning and data science algorithms.

This ensures that no prior knowledge is required to start learning from this course. The content of this course is specially designed to encompass all the concepts that come under the domain of data science. This course not only guides you through the problems and concepts of machine learning but also elaborates how to successfully implement those concepts.

AI Sciences will draw on our expertise in data science and AI to guide you through what matters, and what doesn't.

Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on basics understanding of them.

We'll cover the data science, machine learning, and data mining techniques real employers are looking for, including:

Linear Regression

Support Vector Machine (SVM)

Decision Tree and Random Forest

Logistic Regression

K-Nearest Neighbors (K-NN)

Naive Bayes

K-Means Clustering

Hierarchical Clustering

Evaluating Machine Learning Models Performance

Neural Networks Best

Practices for Data Scientist

and much more!

If you're new in the data science field, don't worry - the course starts with a crash course.

If you're a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the AI industry - this course will teach you the basic techniques used by real-world industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting for?

Enroll now!

Who this course is for:

Beginners who want to approach Machine Learning, but are too afraid of complex math to start
Students and academicians, especially those focusing on Machine Learning
Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way

Citación :

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