CF

Machine Learning Fundamentals

4h 5m 9s
English
Paid

Machine Learning Fundamentals is a 21-lesson 4 hours 5 minutes self-paced course by LunarTech. Advance your career in machine learning with confidence.

Course facts

Lessons
21
Duration
4 hours 5 minutes
Level
All levels
Language
English
Updated
Instructor
LunarTech
Price
Premium
Advance your career in machine learning with confidence. Master the key ML fundamentals that are in demand by employers and acquire the skills necessary to solve real-world problems. Start building a successful future in the tech field today!

Who teaches Machine Learning Fundamentals? LunarTech

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LunarTech is an online tech academy focused on data science, machine learning, and quantitative analysis — covering both the theoretical foundations (linear algebra, calculus, statistics) and the practical Python / SQL toolchain that working data scientists use. The school operates globally with cohort-based and self-paced tracks.

The CourseFlix listing carries twelve LunarTech courses spanning machine-learning theory, deep learning, applied data-science workflows, and the math fundamentals underlying the field. Material is paid and aimed at engineers and analysts transitioning into formal data-science roles or upskilling within them.

What lessons are included in Machine Learning Fundamentals?

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#1: Introduction
All Course Lessons (21)
#Lesson TitleDurationAccess
1
Introduction Demo
06:55
2
1. Machine Learning Basics
15:27
3
2. Bias-Variance Trade-off
07:11
4
3. Overfitting Regularization
15:31
5
4.1 Linear Regression - Causal Analysis (Part 1)
14:51
6
4.2 Linear Regression - (Part 2)
24:25
7
5. Logistic Regression & Maximum Likelihood Estimation (MLE)
15:09
8
6. Linear Discriminant Analysis (LDA)
11:15
9
7. K-Nearest Neighbors (KNN)
11:52
10
8. Decision Trees
16:45
11
9. Bagging
07:54
12
10. Random Forest
08:11
13
11. (Boosting Part 1) Introduction
08:01
14
12. Boosting (Part 2) - AdaBoost
10:00
15
13. Boosting (Part 3) - Gradient Boosting Model (GBM)
10:53
16
14. Boosting (Part 4) - XGBoost
08:08
17
15. Clustering (Part 1) - K-Means & Elbow Method
17:43
18
16. Clustering (Part 2) - Hierarchical Clustering
10:49
19
17. Clustering (Part 3) - DBScan
07:45
20
18. Dimensionality Reduction (Part 1) - Feature Selection
07:32
21
19. Dimensionality Reduction (Part 2) - Principal Component Analysis
08:52
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Frequently asked questions

What prerequisites are needed for this machine learning course?
Before enrolling in this course, students should have a basic understanding of statistics and linear algebra. Familiarity with Python programming will be beneficial, as the course involves implementing machine learning algorithms and concepts programmatically. While the course covers fundamental concepts from the ground up, having these prerequisites will help students grasp the material more effectively.
What projects or practical exercises are included in the course?
Students will engage in practical exercises such as implementing Linear Regression, Logistic Regression, and Decision Trees. The course also includes hands-on projects involving K-Means Clustering and Principal Component Analysis (PCA), where students can apply learned techniques to analyze datasets. These exercises are designed to reinforce theoretical concepts and provide practical experience with machine learning models.
Who is the target audience for this machine learning course?
The course is intended for individuals seeking to gain foundational knowledge in machine learning, particularly those aspiring to start a career in data science or enhance their technical skill set. It is suitable for beginners who have some prior experience in programming and statistics but are new to machine learning concepts and applications.
How does the course depth compare to other machine learning courses?
This course offers a solid foundation in machine learning fundamentals, covering key topics such as Bias-Variance Trade-off, Overfitting Regularization, and Dimensionality Reduction. While it provides a comprehensive overview suitable for beginners, those seeking advanced topics or cutting-edge research areas should consider supplementary courses or resources focused on specialized machine learning techniques.
What specific tools or platforms will be used throughout the course?
The course does not focus on specific machine learning platforms or tools but emphasizes understanding core algorithms and techniques such as K-Nearest Neighbors (KNN), Random Forest, and Boosting methods like AdaBoost and XGBoost. Students will primarily use Python for programming exercises, leveraging libraries like NumPy and SciPy for data manipulation and analysis.
What topics are not covered in this machine learning course?
The course focuses on foundational machine learning concepts and does not cover deep learning topics, neural networks, or large-scale data processing frameworks like Hadoop or Spark. Students interested in these advanced areas should explore additional courses that specialize in deep learning or big data technologies.
How much time should students expect to commit to this course?
While the total runtime of the lessons is not specified, students should be prepared to invest several hours per week to review lesson materials, complete practical exercises, and reinforce their understanding of the concepts. The time commitment will vary based on individual learning pace and prior experience with the subject matter.