Mastering Machine Learning with Python
Mastering Machine Learning with Python
Development,Data Science,Machine Learning
Lectures -67
Duration -12.5 hours
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Course Description
Welcome to "Mastering Machine Learning with Python," a comprehensive course designed to equip learners with the skills and knowledge to excel in machine learning using Python. This course covers a wide range of topics, ensuring you gain a robust understanding of machine learning and its applications.
"Mastering Machine Learning with Python" immerses learners in advanced machine learning algorithms, hypothesis testing, and optimization techniques. You will delve into essential topics such as the basics of matrices, decision trees, random forests, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Naive Bayes classifiers. Explore unsupervised learning techniques, including K-Means clustering and PCA, and gain Time Series Analysis (TSA) proficiency.
This course also covers advanced areas like Natural Language Processing (NLP), Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), and reinforcement learning. Additionally, you will use libraries like Scikit-Learn for machine learning tasks and OpenCV for NLP walkthroughs.
Ideal for data scientists and aspiring machine learning engineers, this course is tailored to help you master machine learning with Python. Embark on your journey to master machine learning with Python and unlock limitless opportunities in your professional and academic pursuits. Enroll today to elevate your skills and gain a competitive edge in today's data-driven world!
Who this course is for:
- Data Scientists
- Aspiring Machine Learning Engineers
- Data Analysts and Researchers
- Software Developers Interested in Machine Learning
- Anyone Seeking Advanced Machine Learning Proficiency
Goals
Deepen Machine Learning Proficiency: Enhances understanding of machine learning algorithms, techniques, and their applications
Master Analytical Tools: Advanced techniques for data analysis and predictive modeling.
Advanced Data Management: Exploring strategies for organizing, cleaning, and transforming data effectively using Python.
Feature Engineering Techniques: Gaining proficiency in creating and selecting the most relevant features for modeling.
Real-World Applications: Applying machine learning skills to practical scenarios, such as financial forecasting, healthcare analytics, and more.
Prerequisites
Basic knowledge of Python programming
Familiarity with basic statistics and linear algebra.
Basic understanding of data structures and control flow.
Curriculum
Check out the detailed breakdown of what’s inside the course
Basics of matrices
2 Lectures
- Scalar ,vector, matrix and arrays 12:39 12:39
- Matrix multiplication 08:58 08:58
Hypothesis Testing
4 Lectures
Decision Tree
4 Lectures
Random Forest
4 Lectures
KNN
4 Lectures
SVM
4 Lectures
Naive Bayes
3 Lectures
Unsupervised Learning
1 Lectures
K-Means
4 Lectures
TSA
5 Lectures
PCA
2 Lectures
NLP
2 Lectures
ANN
3 Lectures
RNN
3 Lectures
Walthrough to NLP
8 Lectures
Open CV - Part 1
9 Lectures
Scikit Learn
5 Lectures
Instructor Details
GreyCampus Inc.
About me
GreyCampus helps people power their careers through skills and certifications. We believe continuous upskilling and certifications is key to sustained success in your career. While older skills are fast becoming less relevant, need for newer in-demand skills is growing exponentially. We believe if you stay skilled, you will stay ahead.
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