Machine learning 101 - (Course also involves a live Project)
Learn the core ideas in machine learning and build your first models
The Python programming language is emphasized in our course. We cover topics such as data acquisition, data analysis, Numpy, Pandas, Matplotlib statistics, visualization, prediction and machine learning with Scikit Learn, we also dive a little into Keras - Natural Language Processing and more.
- Phase 1: Python and database fundamentals
- Phase 2: Statistical applications
- Phase 3: Machine learning and Big Data
- Phase 4: Two weeks of final project work.
1. How Models Work
The first step if you're new to machine learning
2. Basic Data Exploration
Load and understand your data
3.Your First Machine Learning Model
Building your first model. Hurray!
4. Model Validation
Measure the performance of your model ? so you can test and compare alternatives
5. Underfitting and Overfitting
Fine-tune your model for better performance.
6. Classification using machine learning
Using a more sophisticated machine learning algorithm.
7. Linear Regression and Logistic Regression
Computer Science + Maths - theory , exercises + coding challenges
8. Random Forest
Computer Science + Maths - theory , exercises + coding challenges
9. SVM
Learning the toolkit of a Data Scientist
10. Intro to Keras - Natual Language processing
On route to making Chatbots
Pre-requisites
No prior data science experience is required. However, all students will be required to go through some pre-work. Students who take the introductory Python Foundation workshop (Python 101) maybe exempt from pre-work.
No prior data science experience is required. However, all students will be required to go through some pre-work. Students who take the introductory Python Foundation workshop (Python 101) maybe exempt from pre-work.
Conclusion
This is prelude to neural net and Artificial intelligence.
Students will learn everything about building, training models and predicting using Machine learning techniques.
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