Mastering Data Science and Machine Learning Fundamentals

Name: Mastering Data Science and Machine Learning Fundamentals
Rating: 4.5 (1416 reviews)

Data Science & Machine Learning- Data Science, Machine Learning, Regression, Classification and Clustering [THEORY ONLY]

Created byAI Sciences

Last updated 11/2022

English

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

Course content

5 sections • 20 lectures • 1h 50m total length

Welcome1:43
Explore the a-to-z fundamentals of data science and machine learning for beginners, covering introduction, model types like regression, classification, clustering, and evaluation.
Introduction to Data Science6:56
Data science blends computer science, mathematics, and statistics, using machine learning and deep learning to enable analytics and big data applications across healthcare, finance, and transport.
Request for Your Honest Review1:18
Explore the Udemy review system after reviewing remaining sections to judge whether complex concepts are made simple and five-star material; share honest feedback to support updates and learning.

Understand Data Science Terms3:46
Explore data as facts organized in rows of observations and columns of features. Learn numerical and categorical variables, and dependent and independent variables, plus population, sample, outliers, and missing data.
Understand Machine Learning5:42
Explore how artificial intelligence, machine learning, and deep learning relate, and distinguish supervised, unsupervised, and reinforcement learning through features, labels, and observations.
Type of Learning4:18
Explore supervised and unsupervised learning, analyze regression and classification models like linear regression, decision trees, SVR, logistic regression, SVMs, and neural networks, and examine clustering with K mean clustering.
Understand Data Science Modelling4:38
Explore training and test data splits, training a model and evaluating it for generalization, and manage underfitting, overfitting, regularization, cross-validation, and the bias-variance tradeoff.
what is the difference between model Parameters and Hyperparameters1:34
Differentiate model parameters, estimated from historical training data and internal to the model, from hyperparameters, external to the model and specified by the practitioner.

How Linear Regression works?7:10
Explore linear regression, a supervised algorithm that predicts a dependent variable from independent variables using a regression line, coefficients, and least-squares optimization.
How Decision Trees works?9:16
Learn how decision trees classify and regress by splitting data from root to leaves, using entropy and information gain to choose splits and prune to avoid overfitting.
How Bagging and Random Forest work?8:04
Master ensemble learning with bagging and random forest, built from bootstrap samples and feature subsets, to reduce overfishing and boost predictive accuracy; also explore AdaBoost and gradient boosting.
How Support Vector Machine Works?3:45
Explore how the support vector machine classifies data by selecting a hyperplane with the maximum margin, and how mapping to higher dimensions enables separation for linearly non separable data.
Neural Networks Part 18:14
Explore how a neuron processes inputs with weights, bias, and linear or nonlinear activation functions to produce outputs, and how neural networks train on examples to classify data.
Neural Networks Part 212:00
Explore activation functions like sigmoid, tanh, and ReLU in multi-layer perceptrons, and learn how gradient descent and backpropagation minimize cost on non-linear data.
How Logistic Regression works?5:06
Explain how logistic regression models the probability of a binary outcome using the log-odds link function, estimates coefficients by maximum likelihood, and makes predictions with a 0.5 threshold.
How KNN works?3:39
Learn how the k nearest neighbors algorithm, a lazy, instance-based model, uses the k nearest training points and voting to classify unseen data, with Euclidean and Hamming distances guiding selection.
Clustering techniques5:23
Explore unsupervised learning through clustering and k means, comparing distance-based and conceptual approaches, forming data point clusters, and understanding initialization, center updates, and practical criteria for choosing k.

Best Practices for Data Scientist8:52
Learn best practices for data science and machine learning, including data cleaning, data processing, feature engineering and extraction, one hot encoding, binning, and feature scaling to improve model performance.
THANK YOU1:20
Celebrate completing this course and apply new artificial intelligence, machine learning, statistics, and data science concepts in the workplace, while exploring beginner-focused courses designed for new learners.

Requirements

Absolutely no prior knowledge or experience needed. Only a passion to be successful!
Admin permissions to download files

Description

Embark on a Journey into the World of Data Science and Machine Learning!

Welcome to the Mastering Data Science & Machine Learning Fundamentals for Beginners course, a comprehensive and illuminating exploration of the captivating realms of Data Science and Machine Learning!

In today's rapidly evolving landscape, Data Science and Machine Learning are not mere buzzwords; they are the driving forces behind innovation in diverse domains, including IT, security, marketing, automation, and healthcare. These technologies underpin the very foundations of modern conveniences, from email spam filters and efficient Google searches to personalized advertisements, precise weather forecasts, and uncanny sports predictions. This course is your gateway to understanding the magic behind these advancements.

Designed with students and learners in mind, this course aims to demystify complex machine learning algorithms, statistics, and mathematics. It caters to those curious minds eager to solve real-world problems using the power of machine learning. Starting with the fundamentals, the course progressively deepens your understanding of a vast array of machine learning and data science concepts.

No prior knowledge or experience is required to embark on this enriching learning journey. This course not only simplifies intricate machine learning concepts but also provides hands-on guidance on implementing them successfully.

Our esteemed instructors, experts in data science and AI, are your trusted guides throughout this course. They are committed to making each concept crystal clear, steering away from confusing mathematical notations and jargon, and ensuring that everything is explained in plain English.

Here's a glimpse of what you'll delve into:

Mastering Machine Learning Fundamentals
Distinguishing between Supervised and Unsupervised Learning
Unveiling the Power of Linear Regression
Harnessing the Potential of Support Vector Machines (SVM)
Navigating Decision Trees and the Enchanting Realm of Random Forests
Demystifying Logistic Regression
Getting Acquainted with K-Nearest Neighbors (K-NN)
Embracing Naive Bayes
Delving into K-Means Clustering
Exploring the World of Hierarchical Clustering
Assessing Machine Learning Model Performance with Confidence
Venturing into the Realm of Neural Networks
Uncovering Best Practices for Data Scientists
And so much more!

Whether you're a programmer seeking to pivot into an exciting new career or a data analyst with aspirations in the AI industry, this course equips you with essential techniques used by real-world data scientists. These are the skills every aspiring technologist should possess, making your learning journey a vital investment in your future.

So, don't hesitate! Enroll in this course today to begin your transformation into a Data Scientist. Whether you're taking your first steps into this exciting field or you're an experienced data scientist looking to refine your skills, this course is your ticket to mastering Data Science and Machine Learning.

Seize this opportunity to unlock the fascinating world of Data Science and Machine Learning. Enroll now!

List of Keywords:

Data Science
Machine Learning
Beginner's Guide
Fundamentals
Data Analysis
Statistics
Linear Regression
Supervised Learning
Unsupervised Learning
Support Vector Machine
Decision Trees
Random Forest
Logistic Regression
K-Nearest Neighbors
Naive Bayes
Clustering
Performance Evaluation
Neural Networks
Best Practices
Hands-on
Practical Implementation
Data Scientist
AI Industry
Career Transition
Real-world Problems
Plain English Explanation
Expert Instructors
Online Learning
Enroll Now
Comprehensive Course
Beginner-Friendly
Data Analysis Techniques
Python Programming
Machine Learning Models
Learning Path
Algorithmic Concepts
Hands-on Exercises
Interactive Learning
Master Data Science
Build Machine Learning Models

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

Mastering Data Science and Machine Learning Fundamentals

What you'll learn

Explore related topics

Course content

Welcome and Introduction3 lectures • 10min

Preliminary to understand Data Science and Machine Learning5 lectures • 20min

Machine learning Models9 lectures • 1hr 3min

Models Performances1 lecture • 8min

Best Practices for Data Scientist2 lectures • 10min

Requirements

Description

Who this course is for: