Rating 4.3 out of 5 (56 ratings in Udemy)
What you'll learn- Basics to Advanced Machine Learning & Advanced Deep Learning Algorithms with Live Practice Interviews with Experts
- Image Recognition & Keras Deep Learning Neural Network Model Implementation.
- Automated Machine Learning Frameworks & Model Deployment Architectures
- Basic to Advanced Python with Pandas and Flask API creation
- Anomaly Detection Algorithms
- Efficient Feature Engineering & Data Pre-Processing
- Working with Multiple Data …
Rating 4.3 out of 5 (56 ratings in Udemy)
What you'll learn- Basics to Advanced Machine Learning & Advanced Deep Learning Algorithms with Live Practice Interviews with Experts
- Image Recognition & Keras Deep Learning Neural Network Model Implementation.
- Automated Machine Learning Frameworks & Model Deployment Architectures
- Basic to Advanced Python with Pandas and Flask API creation
- Anomaly Detection Algorithms
- Efficient Feature Engineering & Data Pre-Processing
- Working with Multiple Data Sets and Algorithm building in Kaggle Cloud.
DescriptionInterested in the field of Machine Learning? Then this course is for you!
Designed & Crafted by AISolutionExpert with 15 +years of relevant and hands on experience into Training ,Coaching and Development.
Complete Hands-onAI Model Development with Python.
Course Contents are:
Understand Machine Learning in depth and in simple process.
Fundamentals of Machine Learning
Understand the Deep Learning Neural Nets with Practical Examples.
Understand Image Recognition and Auto Encoders.
Machine learning project Life Cycle
Supervised & Unsupervised Learning
Data Pre-Processing
Algorithm Selection
Data Sampling and Cross Validation
Feature Engineering
Model Training and Validation
K -Nearest Neighbor Algorithm
K- Means Algorithm
Accuracy Determination
Visualization using Seaborn
You will be trained to develop various algorithms for supervised & unsupervised methods such as KNN , K-Means , RandomForest, XGBoost model development.
Understanding the fundamentals and core concepts of machine learning model building process with validation and accuracy metric calculation. Determining the optimum model and algorithm.
Cross validation and sampling methods would be understood.
Data processing concepts with practical guidance and code examples provided through the course.
Feature Engineering as critical machine learning process would be explained in easy to understand and yet effective manner.
We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.
