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Fundamentals of Machine Learning

Embark on a Thrilling Voyage from a Non-Technical Background to Becoming an Accomplished Machine Learning Practitioner.

Machine Learning

Blast off into a new world of possibilities with our “Fundamentals of Machine Learning” comprehensive course! It’s your launchpad to the limitless universe of Machine Learning and Data Science. 

This comprehensive course is meticulously designed to take you from a novice to a proficient machine learning maestro, ensuring a deep understanding of machine learning’s bedrock principles.

Equipping you with knowledge of the most popular algorithms that are currently revolutionizing the landscape of AI.

With an extensive suite of 22 meticulously crafted lectures, you will be taken on a panoramic tour of the world of machine learning. Each topic, from the foundational basics to the complex intricacies, is expertly covered, ensuring a comprehensive understanding of this vast and exciting field. So, strap in for your space voyage and prepare for liftoff!

Theory: Let's Set the Coordinates

Blast off into a new world of possibilities with our “Fundamentals to Machine Learning” course! It’s your launchpad to the limitless universe of Machine Learning and stepping stone to the world of AI. 

Lift-off: Machine Learning Basics

Our journey begins with an exploration of Machine Learning basics where you'll grasp the underlying principles of supervised and unsupervised models. As you navigate through this labyrinth of knowledge, you'll learn about evaluation metrics tailor-made for regression, classification, and clustering models used to evaluate the performance of your Machine Learning model.

First Orbit: Bias-Variance Trade-off

As you breach the atmosphere, we'll traverse the intriguing landscapes of Bias-Variance trade-off and Overfitting, shedding light on the delicate balance and common challenges in machine learning.

Overfitting and Regularization

Our voyage continues to the realm of Overfitting and Regularization. You'll understand the challenges of overfitting and learn about the powers of regularization techniques such as Ridge Regression, Lasso regression and other regularization techniques.

Linear Regression and Ordinary Least Squares (OLS)

The spaceship then travels to the one of the simplest but most popular Machine Learning models, Linear Regression, where you'll take a deep dive into the process of Ordinary Least Squares (OLS), its assumptions, and how to test and validate them. Evaluate to its pros and cons.

Logistic Regression and MLE

Your machine learning odyssey continues with a stopover in the Logistic Regression nebula, where you'll grapple with the concepts of odds, log-odds, log-likelihood function and the Maximum Likelihood Estimation (MLE). Evaluate its pros and cons.

Linear Discriminant Analysis (LDA)

Next, we set course for new planets - Linear Discriminant Analysis (LDA). LDA, a classifier with a linear decision boundary, is an incredible technique often used when the classes are separable. As we orbit around LDA, we'll explore its definitions, assumptions, and evaluate its pros and cons, even comparing it with the Logistic Regression.

Exploring K-Nearest Neighbors (KNN)

We will explore the captivating world of KNN, an algorithm that operates on a principle that is simple yet effective. During this expedition, you'll get a grasp of how KNN works step-by-step, its applications in classification problems, and how to determine the right number of neighbors, "k". We will also weigh its pros and cons, preparing you to make an informed decision on when and where to use it.

Space of Trees: Decision Trees

Next, we dive deep into the world of Decision Trees. Understand its intricate definition, become proficient with the step-by-step algorithm, and gain hands-on experience in building your own trees. Master the art of Recursive Binary Splitting and get a firm grasp on evaluating models with Gini Index, Entropy, and RSS. Weigh up the pros and cons of Decision Trees, arming yourself with a balanced understanding.

Ensemble Methods: Bagging, Random Forest, and Boosting

Ensemble methods such as Bagging, Random Forests, and advanced Boosting models like AdaBoost, GBM, and XGBoost will be your next destinations. These methods will equip you with powerful algorithms to improve your models' performance. You will get a grasp of step-by-step guide behind these algorithms and they compare.

Into the Deep Space: Clustering

Next, your interstellar journey will take you to the Clustering galaxies, where you'll be introduced to K-Means, Hierarchical Clustering, and DBSCAN. Each galaxy will reveal novel methods to discover hidden patterns in unlabelled data. We will also weigh its pros and cons, as well as metrics to use and how to Elbow method for K-Means, preparing you to make an informed decision on when and where to use it.

Star Navigation: Dimensionality Reduction

To navigate the multiverse of features efficiently, you will learn about Dimensionality Reduction including various Feature Selection techniques, and the infamous Principal Component Analysis with Elbow Method, a necessary compass to guide your models through the high-dimensional data spaces.

Resampling & Optimization Techniques

Before heading back to earth, one of our final destinations include the lands of Resampling Techniques and Optimization Algorithms for hyper-parameter tuning. Here, you'll learn how to assess and enhance your model's performance using techniques such as various Cross-Validation techniques, and Bootstrapping, as well as all popular optimization models.

Your Machine Learning Journey Starts Now!

Your journey doesn’t end with the course completion!

This course is just your springboard into the exciting world of Machine Learning! Now it’s your turn to take the leap, explore and make waves.

Dive into our exclusive eBooks and real-world case studies for a deep dive into practical Machine Learning applications.

Keep learning, keep growing, and start making a difference with LunarTech.

Let’s revolutionize data science together – Take the leap now!

Bon Voyage Extras
Your Astronaut Survival Kit

The Galactic Interviews: Preparing for The Real World

Your journey doesn't end with the course completion! Our bonus e-book prepares you for the ultimate quest – Machine Learning Interviews. Equipped with answers to the 35 most popular Machine Learning Interview questions with sample answers, this guide will ensure you make a stellar impression.

Bonus E-Book

Stay On Top Of The Field With Our E-Book Containing Free Resources For Further Reading. It's Like Having An Interstellar Library At Your Fingertips. We offer this bonus e-book with free resources for all topics covered as part of the Fundamentals to Machine Learning course.

Ready for Your Cosmic Journey?

Prepare to unravel the mysteries of Machine Learning & AI.  

Your Machine Learning mission begins now - are you ready to launch?

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