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
• 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
• 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