Machine Learning A-Z™: Ai, Python And Mlops

Learn Data Science through a comprehensive course curriculum encompassing essential topics like statistics etc.

What you'll learn

Know which Machine Learning model to choose for each type of problem

Make powerful analysis

Have a great intuition of many Machine Learning models

Master Machine Learning on Python & R

Requirements

Just some high school mathematics level.

Description

Interested in the field of Machine Learning? Then this course is for you!This course has been designed by a Data Scientist and a Machine Learning expert so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.Over 900,000 students world-wide trust this course.We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.This course can be completed by either doing either the Python tutorials, or R tutorials, or both - Python & R. Pick the programming language that you need for your career.This course is fun and exciting, and at the same time, we dive deep into Machine Learning. It is structured the following way:Part 1 - Data PreprocessingPart 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest RegressionPart 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest ClassificationPart 4 - Clustering: K-Means, Hierarchical ClusteringPart 5 - Association Rule Learning: Apriori, EclatPart 6 - Reinforcement Learning: Upper Confidence Bound, Thompson SamplingPart 7 - Natural Language Processing: Bag-of-words model and algorithms for NLPPart 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural NetworksPart 9 - Dimensionality Reduction: PCA, LDA, Kernel PCAPart 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoostEach section inside each part is independent. So you can either take the whole course from start to finish or you can jump right into any specific section and learn what you need for your career right now.Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models.this course includes both Python and R code templates which you can download and use on your own projects.

Overview

Section 1: Introduction to Machine Learning and MLOps

Lecture 1 Introduction to the course

Section 2: Foundational basics of Classical Machine learning from scratch

Lecture 2 Deep Dive into Logistic Regression

Lecture 3 Basics of Natural Language Processing

Lecture 4 Text Preprocessing for word embedding

Section 3: Assumptions and Analysis of Regression Models

Lecture 5 Linear Regression - Analysis of Amazon Fine Food Reviews

Section 4: Tree Based Classification and Regression Models & Methods

Lecture 6 Decision Tree

Lecture 7 Ensembles Models- Random Forest

Section 5: Unsupervised Learning Models - K Means

Lecture 8 K Mean Algorithm

Section 6: Introduction to MLOps & Deep Dive into Production Strategies

Lecture 9 Introduction to Machine learning Operations

Lecture 10 Continuous Integration and Continuous Deployment (CI/CD) and Version controlling

Lecture 11 The application of DevOps principles in data science

Lecture 12 Examining the Different Types of Containers with an examples

Lecture 13 Monitoring and managing containers in a production environment with an example

Lecture 14 Optimize resource usage and efficient deployment and scaling of applications

Section 7: Flight Fare Prediction: Accurate and Timely Estimates for Affordable Travel

Lecture 15 Building Blocks of Regression Techniques

Lecture 16 Introduction of Data & Analytics

Lecture 17 Applying MLops for Flask Application

Anyone interested in Machine Learning.,Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.,Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.,Any people who are not satisfied with their job and who want to become a Data Scientist.,Students who have at least high school knowledge in math and who want to start learning Machine Learning.,Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.