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Practical Machine Learning
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Master Machine Learning by building and training various ML models efficiently with top industry experts and build a stellar portfolio of projects with this beginner-to-expert Practical Machine Learning course online.
₹ 2499.00
₹ 3000.00
17% Discount
#1
See all ranking
Practical Machine Learning
by
Guvi
Master Machine Learning by building and training various ML models efficiently with top industry experts and build a stellar portfolio of projects with this beginner-to-expert Practical Machine Learning course online.
₹ 2499.00
₹ 3000.00
17% Discount
#1
See all ranking
4 Modules
with Certifications
9+ Hours
of Recorded Content
4.5 Rated
by 2001 Learners
4 Modules
with Certifications
9 + Hours
of Recorded Content
4.5 Rated
by 2001Learners
Read all Reviews
What's in it for You?
This comprehensive and practical machine learning course designed by industry experts will teach you all the core concepts that drive machine learning and how to build and train various ML models that are used to build real-world data-driven applications by actually implementing all concepts first-hand. Throughout this course, you will go from from beginner to pro with concepts such as Regression, Gradient Descent, Data Augmentation, Cross-Validation and then onto more advanced topics such as Exploratory Data Analysis (EDA), Feature Engineering, Curse of Dimensionality and so much more. You will be working with one of the most popular ML libraries in Python: Scikit-learn and will learn to write code like a pro. Addedly, you will build as well as train classification and regression models and evaluate their performance on real-world datasets as part of your project experience in the curriculum.
This comprehensive and practical machine learning course designed by industry experts will teach you all the core concepts that drive machine learning and how to build and train various ML models that are used to build real-world data-driven applications by actually implementing all concepts first-hand. Throughout this course, you will go from from beginner to pro with concepts such as Regression, Gradient Descent, Data Augmentation, Cross-Validation and then onto more advanced topics such as Exploratory Data Analysis (EDA), Feature Engineering, Curse of Dimensionality and so much more. You will be working with one of the most popular ML libraries in Python: Scikit-learn and will learn to write code like a pro. Addedly, you will build as well as train classification and regression models and evaluate their performance on real-world datasets as part of your project experience in the curriculum.
Key Features:
Globally Recognised Certification
100% online and Self-paced learning
Full lifetime access to all content
Access to 4 Gamified Practise Platforms
Dedicated Forum Support to clear all your doubts
7 Days refund Policy
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Beginner Module
- Machine Learning Refresher - Intro, Types & Applications
- Machine Learning Refresher - Linear Regression
- Machine Learning Refresher - Logistic Regression
- Machine Learning Project LifeCycle
- ML Model Training Process
- Training a Classification Task - Python Implementation
- Gradient Descent - Error Surfaces
- Gradient Descent - Computation Graphs
- Gradient Descent - Algorithm, Geometric Intuition
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Intermediate Module
- Gradient Descent - Implementation for Linear Regression
- Gradient Descent - Importance of Learning Rate
- Gradient Descent - Common terminology & Hyperparameters
- Gradient Descent - Types
- Python Implementation of end-to-end ML Model Training
- Common Issues during Training & Methods to tackle - 1
- Common Issues during Training & Methods to tackle - 2
- Bias-Variance Tradeoff
- Data Augmentation, Cross-Validation & Regularization
- Early Stopping Method & Implementation
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Advanced Module
- L1 & L2 Regularization Methods
- Implementation showing the effects of Regularization
- Properties A Loss Function Should Have
- Standard Loss functions for Classification
- Standard Loss functions for Regression
- Python Implementation of Loss Functions
- Evaluation of Trained Machine Learning Model
- Evaluation Metrics for Regression Tasks
- Classification Metrics - Accuracy, Confusion Matrix
- Precision, Recall, F1-score & others
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Expert Module
- Python Implementation of Evaluation Metrics
- Exploratory Data Analysis (EDA)
- Feature Engineering - Intro & Significance
- EDA and Feature Engineering in Python - Part 1
- EDA and Feature Engineering in Python - Part 2
- EDA and Feature Engineering in Python - Part 3
- Curse of Dimensionality
- Dimensionality Reduction & PCA Intro
- Principal Component Analysis - Foundations
- Principal Component Analysis - Calculation with Example
- PCA demo on MNIST dataset
- K-Nearest Neighbors Algorithm
- KNN Implementation from scratch
Course Offerings
Certificate you will get
IITM Pravartak certified Python certification.
Certificates are globally recognized & they upgrade your programming profile.
Certificates are generated after the completion of course.
After this Course
Gain strong knowledge of Machine Learning concepts and how to implement them.
Learn to program in Python like a pro.
Understand and implement Exploratory Data Analysis or EDA.
Implement a number of different Machine Learning algorithms and monitor their output on actual live data.
Understand and spot Common Issues during model training as well as how to tackle them.
Implement the KNN algorithm from scratch and build your first ML project.
Pre Requsites
Some experience with the Python programming language.
Basic knowledge of Machine Learning and a few algorithms would be a big plus.
Course is for
Freshers
Professionals
Students
FAQ's
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What is the overview of GUVI’s Practical Machine Learning certification course?Well, you will not only learn about Machine Learning and its various algorithms, but also how to actually implement them along with our industry experts and build various projects which will help you gain hands-on experience. Go pro with concepts such as Regression, Gradient Descent, Data Augmentation, Cross-Validation as well as Exploratory Data Analysis (EDA) in this comprehensive practical machine learning online course.
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Why learn the Practical Machine Learning course?Learning Practical Machine Learning will teach you in-demand skills in data analysis, pattern recognition, and predictive modeling. It equips you with tools to make data-driven decisions, automate tasks, and solve complex problems. This course is essential in today's data-driven world, opening doors to careers in data science, AI, and more, making it a wise investment in your future.
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Why choose GUVI for learning the Practical Machine Learning course?The Practical Machine Learning course offered by GUVI provides an off-beat upskilling experience where you learn by doing. So, you’re not just watching our mentors teach you, but you will be actually building and training ML models with them. The curriculum is one of the best in the industry covering topics for all beginners and eventually taking you to an advanced level with ease.
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What are the benefits of earning Practical Machine Learning certification?Above all, this practical machine learning certification enhances your job prospects by demonstrating your ML expertise. It also equips you with practical skills to tackle real-world problems, making you valuable to employers. Additionally, it provides networking opportunities within the machine learning community, fostering professional growth and knowledge exchange.
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Is the Practical Machine Learning course difficult to learn?However cliche the answer might sound, it is true. The level of difficulty depends entirely on your dedication and on us. But trust us, we’ve made it as simple for you as it gets. With this practical machine learning course by GUVI, you will learn by actually building projects and not just looking at mentors teach you concepts. However, for beginners, the learning curve can be steep. The key is consistent practice and seeking help through our forum whenever needed.
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How long does it take to learn Practical Machine Learning?You can master machine learning through our highly accredited online course at your own pace and make efficient progress. But make sure to build a progressive learning routine. So, it completely depends on your learning pace but somewhere around 4 - 6 weeks should be the average amount of time you would need.
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