What is Machine Learning?

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

The roadmap diverges into 5 Steps:

Step 1 : Type of learning

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning

1. Supervised Learning:

• Supervised Machine learning is a process of providing input data as well as correct output data to the machine learning model.

2. Unsupervised Learning:

• Unsupervised machine learning algorithms to analyze and cluster unlabeled datasets.

3. Reinforcement Learning:

• Reinforcement machine learning is a machine learning model that is similar to supervised learning, but the algorithm isn’t trained using sample data.

Step 2 : Real World Application

• Self - Driving Car

  A self-driving car, also known as an autonomous vehicle (AV), autonomous car, driver-less car, or robotic car (robo-car), is a car incorporating vehicular automation, that is, a ground vehicle that is capable of sensing its environment and moving safely with little or no human input.

• Chatbot

  A chatbot is software that simulates human-like conversations with users via text messages on chat. Its key task is to help users by providing answers to their questions

• Churn Prediction Movie

  It’s a predictive model that estimates — at the level of individual customers — the propensity (or susceptibility) they have to leave

• Diagnosis and Etc.

  Diagnosis is the identification of the nature and cause of a certain phenomenon. Diagnosis is used in many different disciplines, with variations in the use of logic, analytics, and experience, to determine "cause and effect".

Step 3 : Process of Applied ML

1. Data Cleaning / Exploration / Preparation

Data cleaning is one of the important parts of machine learning. It plays a significant part in building a model. It surely isn’t the fanciest part of machine learning and at the same time, there aren’t any hidden tricks or secrets to uncover

• Feature Engineering

  Feature engineering is the process of using domain knowledge to extract features (characteristics, properties, attributes) from raw data.

• Outlier Treatment

  Outliers are nothing but data points that differ significantly from other observations. They are the points that lie outside the overall distribution of the dataset. Outliers, if not treated, can cause serious problems in statistical analyses.

• Missing Value Treatment

  Missing value treatment is one of the most important steps in the data pre-processing. It includes identifying missing value and treating them in a way that minimum amount of information is lost

2. Data Modeling

Data modeling is the process of creating a visual representation of either a whole information system or parts of it to communicate connections between data points and structures

3. Model Validation

Model validation is alluded to as the procedure where a trained model is assessed with a testing data set

4. Model Evaluation

Model evaluation is the process of using different evaluation metrics to understand a machine learning model’s performance, as well as its strengths and weaknesses

5. Model Deployment

Model deployment is simply the engineering task of exposing an ML model to real use. The term is often used quite synonymously with making a model available via real-time APIs.

Step 4 : Practice

1. Join Communities

2. Learn from Experts

3. Participate Kaggle Competition

4. Listen to Podcast

5. Subscribe Newsletters

6. Learn Tools & Technologies

• Python

  • Python is an interpreted, object-oriented, high-level programming language with dynamic semantics.

    • Keras
    • TensorFlow
    • Scikit-Learn
    • Pandas
    • NumPy

• R

  • R is a programming language and software environment for statistical analysis, graphics representation and reporting

    • mlr
    • dplyr
    • caret

Step 5 : Theory

1. Multivariate Calculus

• Multivariate calculus is extremely important in machine learning because we use optimization in order to improve our neural network. In particular, we use variations of gradient descent to optimize a neural network.

2. Algorithm & Complexity

• Algorithmic complexity is a measure of how long an algorithm would take to complete given an input of size n.

3. Optimization

• Optimization, also known as mathematical programming, collection of mathematical principles and methods used for solving quantitative problems in many disciplines

4. Probability Theory & Statistics

• Probability theory is a field of mathematics and statistics that is concerned with finding the probabilities associated with random events. There are two main approaches available to study probability theory. These are theoretical probability and experimental probability.

5. Linear Algebra

• Linear algebra is the study of linear combinations. It is the study of vector spaces, lines and planes, and some mappings that are required to perform the linear transformations.