Data Science & Machine Learning- Data Science, Machine Learning, Regression, Classification and Clustering [THEORY ONLY]
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
A comprehensive course that will teach you how Data Science and Machine Learning Work.
Welcome to Mastering Data Science & Machine Learning Fundamentals for Beginners!
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 sports prediction – would be impossible. This course is the starting point you've been looking 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.
No prior knowledge is required to start learning from this course. The course not only guides you through the problems and concepts of machine learning but also elaborates on how to implement those concepts successfully.
We 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.
We'll cover the data science, machine learning, and data mining concepts and techniques which you are looking for, including:
Basics of Machine Learning
Supervised Vs. Unsupervised Learning
Support Vector Machine (SVM)
Decision Tree and Random Forest
K-Nearest Neighbors (K-NN)
Evaluating Machine Learning Models Performance
Best Practices for Data Scientist
and much more!
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 needs to know about, so start learning today?
Become a Data Scientist by enrolling in this course. Even if you are a novice in this field, you will find this illustrative course informative, useful, and helpful. And if you aren't new to data science, you'll still find also this course immensely beneficial.