Machine learning is becoming over the modern data-driven world and it is a growing technology among many companies to extensively support many fields, such as search engines, robotics, self-driving cars, and so on. Here in this course you can explore various real-time scenarios where you can use machine learning.
This course will be making you to understand and implement all Machine learning algorithms with exciting examples. Every trainer will be delivering machine learning algorithms followed by practical session. Most importantly you will be learning from trainers Bottom level to Top level all algorithms, such as Linear Regression, Logistic Regression, Support Vector Machines, Principal Component Analysis, Time Series Analysis Deep Neural Networks, and so on. This is going to friendly to Python programmers and data analyzers to play around with the code to implement machine learning techniques.
Python | Machine Learning | ML and Adaptive Intelligence |
---|---|---|
Fundamentals | Getting Started in Machine Learning | Modeling Relationships Between Variables Using Regression |
Lists | Understanding the Machine Learning Workflow | Understanding Simple Regression Models |
Functions | Asking the Right Question | Implementing Simple Regression Models in Python |
Packages | Preparing Your Data | Understanding Multiple Regression Models |
Numpy | Selecting Your Algorithm | Implementing Multiple Regression Models in Python |
MatplotLib | Training the Model | Decision Trees |
Dictionaries & Pandas | Testing Your Model’s Accuracy | Support Vector Machine |
Control flow and Filtering | K-Means & Hierarchical Clustering | |
Loops | Dbscan | |
Case Study: Hacker Statistics | Content-Based Recommender Systems |