This app has been prepared for professionals aspiring to learn the complete picture of machine learning and artificial intelligence. This tutorial caters the learning needs of both the novice learners and experts, to help them understand the concepts and implementation of artificial intelligence and machine learning.

Who this machine learning course is for:
Anyone interested in Machine Learning. Students who have at least high school knowledge in math and who want to start learn Machine Learning.

Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.

Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.

- Any students in college who want to start a career in Data Science.
- Any data analysts who want to level up in Machine Learning.
- Any people who are not satisfied with their job and who want to become a Data Scientist.
- Any people who want to create added value to their business by using powerful Machine Learning tools.

In this App You Will Learn
- Why Choose Python for Machine Learning
- Machine Learning Roadmap
- Learn Python 3 for Machine Learning
- Learn Artificial Intelligence
- Introduction to Machine Learning
- Learn TensorFlow for machine learning
- Learn Pytorch guide
- Artificial Intelligence complete guide
- Learn Deep Learning
- Learn Machine Learning Complete Guide
- Machine Learning Projects and Examples
- Python 3 tutorials


We'll learn in machine learning
- Concepts
-Types of learning
-Supervised Learning
- Unsupervised Learning
- Data pre-processing, analysis and visualization
- Training data and test data
- Applications
- Regression
- Algorithms
- decision tree algorithm
- Support vector machines (SVM)
- Random forest
- Dimensional reduction algorithm
- boosting algorithms

Artificial Intelligence
- Introduction to Artificial Intelligence
- Intelligent systems
- Agents and environments
- Popular search algorithms
- Fuzzy logic systems
- Natural language processing
- Expert systems
- Robotics
- Neural networks

Also learn more about deep learning , Neural Network in detail