__Python With Machine Learning__

Machine Learning with Python. Machine learning is a branch in software engineering that reviews the plan of calculations that can learn. Normal undertakings are idea learning, work learning or "prescient demonstrating", bunching and finding prescient examples.

Module 1 - Introduction to Machine Learning

• What is Data Science?

• What does Data Science involve?

• Era of Data Science

• Business Intelligence vs Data Science

• Life cycle of Data Science

• Tools of Data Science

• Introduction to Big Data and Hadoop

• Introduction to R

• Introduction to Spark

• Introduction to Machine Learning

Module 2 - Statistical Inference

• What is Statistical Inference?

• Terminologies of Statistics

• Measures of Centers

• Measures of Spread

• Probability

• Normal Distribution

• Binary Distribution

Module 3 - Data Extraction, Wrangling and Exploration

• Data Analysis Pipeline

• What is Data Extraction

• Types of Data

• Raw and Processed Data

• Data Wrangling

• Exploratory Data Analysis

• Visualization of Data

Module 4 - Introduction to Machine Learning

• What is Machine Learning?

• Machine Learning Use-Cases

• Machine Learning Process Flow

• Machine Learning Categories

• Supervised Learning

o Linear Regression

o Logistic Regression

Module 5 - Classification

• What is Classification and its use cases?

• What is Decision Tree?

• Algorithm for Decision Tree Induction

• Creating a Perfect Decision Tree

• Confusion Matrix

• What is Random Forest?

• What is Navies Bayes?

• Support Vector Machine: Classification

Module 6 - Unsupervised Learning

• What is Clustering & its Use Cases?

• What is K-means Clustering?

• What is C-means Clustering?

• What is Canopy Clustering?

• What is Hierarchical Clustering?

Module 7 - Recommender Engines

• What is Association Rules & its use cases?

• What is Recommendation Engine & it’s working?

• Types of Recommendation Types

• User-Based Recommendation

• Item-Based Recommendation

• Difference: User-Based and Item-Based Recommendation

• Recommendation Use-case

Module 8 - Text Mining

• The concepts of text-mining

• Use cases

• Text Mining Algorithms

• Quantifying text

• TF-IDF

• Beyond TF-IDF

Module 9 - Time Series

• What is Time Series data?

• Time Series variables

• Different components of Time Series data

• Visualize the data to identify Time Series Components

• Implement ARIMA model for forecasting

• Exponential smoothing models

• Identifying different time series scenario based on which different Exponential Smoothing model can be applied

• Implement respective ETS model for forecasting

Module 10 - Deep Learning

• Reinforced Learning

• Reinforcement learning Process Flow

• Reinforced Learning Use cases

• Deep Learning

• Biological Neural Networks

• Understand Artificial Neural Networks

• Building an Artificial Neural Network

• How ANN works

• Important Terminologies of ANN’s