Skip to main content
Unlocking Machine Learning; Beginner`s Guide to Essential ML Algorithms

Unlocking Machine Learning; Beginner`s Guide to Essential ML Algorithms

$199.00
When you get access:
Course access is prepared after purchase and delivered via email
How you learn:
Self-paced • Lifetime updates
Your guarantee:
30-day money-back guarantee — no questions asked
Who trusts this:
Trusted by professionals in 160+ countries
Toolkit Included:
Includes a practical, ready-to-use toolkit with implementation templates, worksheets, checklists, and decision-support materials so you can apply what you learn immediately - no additional setup required.
Adding to cart… The item has been added

Unlocking Machine Learning: Beginner's Guide to Essential ML Algorithms



Course Overview

Welcome to Unlocking Machine Learning: Beginner's Guide to Essential ML Algorithms, an interactive and comprehensive course designed to introduce you to the world of machine learning. In this course, you'll learn the fundamentals of machine learning and explore the most essential algorithms used in the industry today.



Course Objectives

  • Understand the basics of machine learning and its applications
  • Learn the most essential machine learning algorithms
  • Develop skills in data preprocessing, model selection, and evaluation
  • Apply machine learning concepts to real-world problems
  • Receive a certificate upon completion of the course


Course Curriculum

Module 1: Introduction to Machine Learning

  • What is machine learning?
  • Types of machine learning: supervised, unsupervised, and reinforcement learning
  • Machine learning applications: image classification, natural language processing, recommender systems
  • Key concepts: data, models, algorithms, and evaluation metrics

Module 2: Data Preprocessing

  • Data types: numerical, categorical, text, and image data
  • Data preprocessing techniques: normalization, feature scaling, encoding
  • Handling missing values: imputation, interpolation, and extrapolation
  • Data visualization: plots, charts, and heatmaps

Module 3: Supervised Learning Algorithms

  • Linear Regression: simple and multiple linear regression
  • Logistic Regression: binary and multiclass classification
  • Decision Trees: classification and regression trees
  • Random Forests: bagging and boosting
  • Support Vector Machines (SVMs): linear and non-linear classification

Module 4: Unsupervised Learning Algorithms

  • K-Means Clustering: centroid-based clustering
  • Hierarchical Clustering: agglomerative and divisive clustering
  • Principal Component Analysis (PCA): dimensionality reduction
  • t-Distributed Stochastic Neighbor Embedding (t-SNE): non-linear dimensionality reduction

Module 5: Model Evaluation and Selection

  • Evaluation metrics: accuracy, precision, recall, F1 score, mean squared error
  • Cross-validation: k-fold and leave-one-out cross-validation
  • Model selection: grid search, random search, and Bayesian optimization
  • Hyperparameter tuning: learning rate, regularization, and batch size

Module 6: Real-World Applications

  • Image classification: convolutional neural networks (CNNs)
  • Natural language processing: text classification, sentiment analysis, and language modeling
  • Recommender systems: collaborative filtering and content-based filtering
  • Time series forecasting: ARIMA, LSTM, and Prophet


Course Features

  • Interactive: Engage with interactive simulations, quizzes, and games to reinforce your learning
  • Comprehensive: Cover the fundamentals of machine learning and explore the most essential algorithms
  • Personalized: Receive personalized feedback and recommendations based on your progress
  • Up-to-date: Stay current with the latest developments in machine learning
  • Practical: Apply machine learning concepts to real-world problems and projects
  • Real-world applications: Explore the applications of machine learning in various industries
  • High-quality content: Learn from expert instructors and high-quality video lessons
  • Certification: Receive a certificate upon completion of the course
  • Flexible learning: Learn at your own pace and on your own schedule
  • User-friendly: Navigate the course with ease and access course materials on any device
  • Mobile-accessible: Access the course on your mobile device or tablet
  • Community-driven: Join a community of learners and instructors to ask questions and share knowledge
  • Actionable insights: Gain practical insights and skills to apply in your career or personal projects
  • Hands-on projects: Work on hands-on projects to apply machine learning concepts
  • Bite-sized lessons: Learn in bite-sized chunks with short video lessons
  • Lifetime access: Access the course materials for a lifetime
  • Gamification: Engage with gamification elements to make learning fun and engaging
  • Progress tracking: Track your progress and stay motivated


Certificate of Completion

Upon completing the course, you'll receive a Certificate of Completion. This certificate is a testament to your hard work and dedication to learning machine learning. You can share it on your resume, LinkedIn profile, or with your employer to demonstrate your skills and knowledge in machine learning.

!