The course Certificate in Python Machine Learning is designed to make students understand the industry needed concepts of machine learning to become a skilled data scientist. This online course is a course of 6 months duration, divided into small modules to make a better experience. So, This makes it the best python course for you.
What will you learn if you enrol for this online python certification?
You will get python online tutorial and after completion of this course you will be able to:
- Building Good Training Datasets,
- Data Preprocessing, Compressing Data,
- Using scikit-learn,
- Model Evaluation and Hyperparameter Tuning,
- Combining Different Models for Ensemble Learning,
Further, you can check the syllabus of the course.
Why should you learn a python programming language?
Python is a high-level programming language. Which runs on the cloud and this software used by many companies for data science, machine learning, algorithms, data mining. It’s popularity increasing every day because of its features and versatility. Here are the few of these companies are using python such as Google, Facebook, Instagram, etc.
What are the essential requirements for this python certification course?
You do not need any prior experience. As this Python online course is designed in such a way that anyone can understand the intermediate or beginner level.
Who should take this course?
If you are a student, developers or analytic manager who are willing to make a bright career in data science. Or, any person who has a relevant interest can join this certificate in python online.
|Maximum Duration||6 Months|
|Topics||Giving Computers the Ability to Learn from Data, Training Simple Machine Learning Algorithms for Classification, A Tour of Machine Learning Classifiers Using scikit-learn, Building Good Training Datasets – Data Preprocessing, Compressing Data via Dimensionality Reduction, Learning Best Practices for Model Evaluation and Hyperparameter Tuning, Combining Different Models for Ensemble Learning, Applying Machine Learning to Sentiment Analysis, Embedding a Machine Learning Model into a Web Application, Predicting Continuous Target Variables with Regression Analysis, Working with Unlabeled Data – Clustering Analysis, Implementing a Multilayer Artificial Neural Network from Scratch, Parallelizing Neural Network Training with TensorFlow, Going Deeper – The Mechanics of TensorFlow, Classifying Images with Deep Convolutional Neural Networks, Modeling Sequential Data Using Recurrent Neural Networks, Generative Adversarial Networks for Synthesizing New Data, Reinforcement Learning for Decision Making in Complex Environments|