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Complete Machine Learning Course with Python for beginners

What you will learn

Master Machine Learning on Python

Make powerful analysis

Make accurate predictions

Make robust Machine Learning models

Use Machine Learning for personal purpose

Build an army of powerful Machine Learning models and know how to combine them to solve any problem

Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA

Clean your input data to remove outliers

Description

Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That’s just the average! And it’s not just about money – it’s interesting work too!

If you’ve got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry – and prepare you for a move into this hot career path.

Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won’t find academic, deeply mathematical coverage of these algorithms in this course – the focus is on practical understanding and application of them. At the end, you’ll be given a final project to apply what you’ve learned!


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Our Learner’s Review: Excellent course. Precise and well-organized presentation. The complete course is filled with a lot of learning not only theoretical but also practical examples. Mr. Risabh is kind enough to share his practical experiences and actual problems faced by data scientists/ML engineers. The topic of “The ethics of deep learning” is really a gold nugget that everyone must follow. Thank you, 1stMentorΒ  and SelfCode Academy for this wonderful course.

English
language

Content

Foundation
Introduction
Introduction to Machine Learning
Introduction to Machine Learning
Applied Statistics
Statistics 101
Descriptive Statistics
Descriptive Statistics (Part-2)
Measures of Spread
Probability
Conditional Probability
Probability Distribution
Hypothesis Testing
ntroduction to Python
Python Installation
Python IDE
Python_Basics
Python Basics II
Data Structures
Numpy
Pandas
Data Visualisation
Data Transformation
Let’s dig Machine Learning
Machine Learning Intro
Regression
Linear Regression
Classification
Logistic Regression
KNN
NaΓ―ve Bayes
SVM
Decision Tree
Clustering
K-means
Hierarchical Clustering
DBScan
Ensemble ML
Bagging
Boosting
Our Project (Recomendation System)
PCA
Recommendations System