Beginner
1 hrs 24 min
4.14
243
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Lead Academy PTE
License No. 283870
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Machine Learning Basics with Python
Welcome to the Machine Learning Course, brought to you by LEAD Academy!
Are you ready to explore the exciting world of artificial intelligence (AI) and machine learning? This course will introduce you to the fundamental concepts and techniques that drive modern data analysis and prediction.
Why Is This Course Important?
In today's digital age, understanding machine learning is essential for anyone interested in AI, technology and data. This course empowers learners of all backgrounds to grasp the basics of machine learning and apply them to real-world scenarios. Whether you're new to the field or looking to expand your knowledge, this course is for you.
Who Is This Course For?
This course is designed for:
- Beginners curious about machine learning and AI
- Tech enthusiasts eager to understand the foundations of data analysis
- Professionals seeking to enhance their skills in predictive modelling
- Students interested in exploring the possibilities of AI-driven technology
- Anyone keen on understanding how data shapes our digital world
Why Should You Enroll?
By enrolling in this course, you will:
Gain a solid understanding of machine learning principles and algorithms.
Discover why Python is the preferred programming language for machine learning.
What Will You Learn?
Throughout this course, you'll:
- Explore the basics of machine learning with practical examples.
- Understand the importance of Python for machine learning applications.
- Learn about essential techniques like regression, classification, and clustering.
- Dive into real-world datasets to build predictive models and make informed decisions.
- Gain hands-on experience with Python libraries like Scikit-Learn and how to interpret data using tools like the Confusion Matrix and Cross-Validation.
- Explore popular regression techniques like Ridge and Lasso Regression.
- Master classification algorithms such as Naive Bayes and Decision Trees.
- Dive into the world of clustering with K-means Clustering.
- Understand the power of ensemble methods like Random Forest.
- Scikit-learn for machine learning.
Join us on this exciting journey into the realm of machine learning and AI. Enroll today and unlock the potential of data-driven insights in the digital age. Your journey to becoming a machine learning expert starts here!
Complete This Course and Get Your certificate!
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What Will I Learn?
- ML Basics
- Python ML
- Data Analysis
- Model Building
- Algorithm Mastery
- Hands-on Skills
- Python Libraries
- Predictive Modeling
Course content
- 1 Chapters
- 16 Lessons
- 1 hrs 24 min
Course Overview
Free Preview
00:00:56
Introduction to Machine Learning
00:03:03
The Best Programming Language for Machine Learning
00:02:12
Confusion Matrix
00:05:55
Cross Validation
00:05:13
Ridge vs Lasso Regression
00:05:52
Linear Regression
00:04:32
K-means Clustering
00:05:02
Implementation of K-means Clustering
00:06:23
Naive Bayes Algorithm
00:05:36
Practical Implementation of Naive Bayes
00:08:58
Decision and Classification Tree
00:08:41
IRIS Dataset Prediction
00:07:15
Random Forest Algorithm
00:05:55
Digit Classification
00:04:34
Principal Component Analysis (PCA)
00:04:29
Final Quiz
Pre Requisites
- Basic Python Knowledge
- Keep video resolution at 1080p
- Basic Computer Skill
Review
4.14
Mohammad Nazmul Hoq
Mahedi Hassan
Md Al Amin
Siddique
Anonymous user
Zisan Ahmed
Md Saazid Bin Saif
Nice
Minhaz Ebna Jalal
Thank you
Fahmida Akter
I'm a Tech Enthusiast person for that I am pursuing ML with Python course with Instructor Sara Karim Mam. Video Quality of the Course was overall good but the timing was short. Instructor presentation and pronunciation overall okay. The main Problem is can't understand PyTorch's implementation of ML such as class 8 Implementation of K-means Clustering, class 11 IRIS Dataset Prediction and class: 10 Practical Implementation of Naive Bayes using Python. Otherwise, The course was Good for beginners who are interested in learning ML. Thank you.
Reply from Instructor
Thank you for taking the time to share your feedback with me. I'm glad to hear that you found the course beneficial and that you enjoyed the video quality and overall content.
I understand that you faced some challenges with the PyTorch implementation, in some classes. PyTorch is a powerful and widely used open-source deep learning framework that allows for the flexible implementation of machine learning algorithms, especially in neural networks and deep learning. It provides tools like tensors (similar to NumPy arrays) and automatic differentiation, which make it easier to build and train complex models.
In Class 8 (K-means), Class 10 (Naive Bayes), and Class 11 (IRIS Dataset), PyTorch was used to illustrate these concepts with a focus on tensor operations and neural network training. If you need more clarification, feel free to ask—I’m here to help!
Adwit Kanti Routh
Good
Rittique Basak
This course is great for getting general idea for ML. I wish there were more projects or code examples from the very beginning of course to see the implementation and get more clear idea. Overall it was a decent course. I need the slides and other resoruces
Reply from Instructor
Thank you for your feedback. Stay connected.
Anonymous user
Somewhat good for beginers.
Anonymous user
Faria Akter
BDT 500 ৳1000
Course Duration
1 hrs 24 min
Course Level
Beginner
Students Enrolled
243
Language
English, Bangla
This course includes
20 Quizzes
1 Free Lessons
36 Months Access
Free Exercise Files & Downloadable Resources
Shareable Certificate of Completion
Access on Mobile, Tablet and PC
Facebook Community Group Support
100% online course
Student Helpline
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