Fundamentals of Machine Learning with Python Implementation

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What you'll learn
Use Python for Data Science and Machine Learning
Implement Machine Learning Algorithms
Handle advanced techniques like Dimensionality Reduction
Know which Machine Learning model to choose for each type of problem
Basics of Reinforcement Learning
Linear Regression
Logistic Regression
Clustering
SVM
Neural Network Concept
Random Forest
PCA and SVD

Des--cription
In this course, students will acquire a good understanding of basic concepts of machine learning. The course also introduces students to deep learning (neural nets) and also artificial intelligence. The concepts are developed from scratch to make students well equipped with all the basics and math involved with all machine learning algorithms
Some concepts we cover include
Various types of learning like supervised, unsupervised and reinforcement learning.
Various supervised learning algorithms like linear and logistic regression.
Clustering techniques.
A brief introduction to Neural Nets.
Parameter tuning, data visualization and accuracy estimation techniques
Reinforcement learning techniques like Q-learning and SARSA
Deciding which algorithm fits for a given problem
Knowing all of these techniques will give an edge to the developer in order to solve many real world problems with high accuracy.

Who this course is for?
Anyone interested in Machine Learning.
Students who want to start learning 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 computer science student hoping to broaden their skillset
Anyone interested in Machine Learning Including business leaders, managers, app developers, consumers - you!


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