Description

Machine Learning is simply recognizing patterns in your data to be able to make improvements and intelligent decisions on its own.

Python

• Introduction to Python
• Python Introduction and IDE
• Basic Commands in Python
• Objects, Number and Strings
• Objects, List, Tuples and Dictionaries
• If_else and For_loop
• Functions and Packages
• Important Packages
• Python built-in libraries
• Python third party libraries
• Object oriented programming
• classes

Introduction to machine learning

• Introduction to analytics and machine learning
• Why machine learning ?
• Why Python ?
• Python stack for data science

Descriptive analytics

• Working with DataFrames in Python
• IPL dataset description using dataframe in python
• Loading Dataset into pandas dataframe
• Displaying first few records of the dataframe
• Finding summary of the dataframe
• Slicing and indexing of dataframe
• Value counts and cross tabulations
• Sorting dataframe by column values
• Creating new columns
• Grouping and aggregating
• Joining dataframes
• Renaming columns
• Apply operations to multiple columns
• Filtering Records based on conditions
• Removing a column or a row from a dataset
• Handling Missing values
• Exploration of Data using Visualization
• Drawing plots
• Bar chart
• Histogram
• Distribution or density Plot
• Box plot
• Comparing distribution
• Scatter plot
• Pair Plot

Probability distributions and hypothesis tests

• Overview
• Probability Theory – Terminology
• – Random Experiment
• –  Sample space
• – Event
• Random Variables
• Binomial Distribution
• Poisson Distribution
• Exponential Distribution
• Normal Distribution
• Central Limit Theorem
• Hypothesis Test
• Analysis of Variance

Linear regression

• Simple linear Regression
• Steps in building a regression model
• Building a simple linear regression model
• Model diagnostics
• Multiple Linear regression

Classification problems

• Classification Overview
• Binary Logistic Regression
• Credit Classification
• Gain Chart and lift chart
• Classification Tree

Advanced machine learning

• Overview
• Gradient Descent Algorithm
• Advanced regression models
• Advanced machine learning algorithms

Clustering

• Overview
• How does clustering work ?
• K means clustering
• Creating product segments using clustering
• Hierarchical clustering

Forecasting

• Forecasting overview
• Components of time series data
• Moving Average
• Decomposing time series

Support Vector Machines

• Overview
• Kernel tricks
• Example

Recommender systems

• Overview
• Association Rules
• Collaborative filtering
• Using surprise Library

Text analytics ( NLTP)

• Overview
• Sentiment Classification
• Naïve-Bayer’s model for sentiment Classification
• Using TF-IDF victorizer

Neural Networks

• Neural Networks Introduction
• Activation functions
• Optimizers
• Loss functions

Deploying application in Azure

Deploying ML application in AWS

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Contact the L&D Specialist at Locus IT.

Locus Academy has more than a decade experience in delivering the training/staffing on Machine Learning for corporates across the globe. The participants for the training/staffing on Machine Learning  are extremely satisfied and are able to implement the learnings in their on going projects.