Data Science & Machine Learning Training Course Online Course

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Data Science & Machine Learning Training Course

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English

Course Description

Master’s in Data Science and Machine Learning will help you master the skills required to become an expert in this domain. Master skills such as Python, ML algorithms, statistics, supervised and unsupervised learning, etc. to become a successful professional in this popular technology. Data Science with Python course helps you learn the python programming required for Data Science. Data Science & Machine Learning mainly focuses on the enhancement and development of the computer programs, which has the property to get changed when it comes in the interaction to the new data. However, this is a kind of artificial intelligence, the Introduction to Machine Learning course enlightens the candidates with the algorithms that proves to be helpful for the IP professionals in analysing the data set with ease. Perhaps the most popular data science methodologies come from machine learning.

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What you’ll learn

Live Class Practical Oriented Training
Timely Doubt Resolution
Dedicated Student Success Mentor
Certification & Job Assistance
Free Access to Workshop & Webinar
No Cost EMI Option
Perform scientific & technical computing using SciPy package & its sub-packages such as Integrate, Optimize, Statistics,...
In-depth understanding of supervised & unsupervised learning models like linear & logistic regression, clustering, dimen...
Learn how to implement the unsupervised learning algorithms, including deep learning, clustering, & recommendation syste...
Develop an understanding classification data and models
Gain important experiences into signs, pictures, and sounds with SciPy, scikit-picture, and OpenCV
Analyze information with Bayesian or frequentist insights (Pandas, PyMC, & R), & gain from genuine information through A...

Covering Topics

Lecture 1-15 Python Programming

Lecture 16-22 Introduction To Statistics

Lecture 23-24 Pandas

Lecture 25-27 NumPy

Lecture-28 Introduction to Machine Learning

Lecture 29-30 Machine Learning and Data Science Framework

Lecture-31 Data Science Environment Setup

Lecture 32-35 Matplotlib + Seaborn: Plotting and Data Visualization

Lecture 36-40 Scikit-learn: Creating Machine Learning Models

Lecture 41-45 Scikit-learn - Regression Model

Lecture 46-48 Scikit-learn – Classification

Lecture 49-50 K Means Clustering

Lecture 51-53 Text Mining

Lecture 54-58 Basic Time Series Forecasting

Lecture 59-60 Principal Component Analysis (PCA)

Case Studies

Curriculum

  Lecture 1-15 Python Programming
Live Lecture 
·         Introduction of Python

·         The Companies using Python

·         Different Applications where Python is used

·         Discuss Python Scripts on UNIX/Windows

·         Values, Types, Variables

·         Operands and Expressions

·         Conditional Statements

·         Loops

·         Command Line Arguments

·         Writing to the screen

·         Sequences and File Operations
·         Python files I/O Functions

·         Numbers

·         Strings and related operations

·         Tuples and related operations

·         Lists and related operations

·         Dictionaries and related operations

·         Sets and related operations

·         Functions, OOPs, Modules, Errors and Exceptions
·         Functions

·         Function Parameters

·         Global Variables

·         Variable Scope and Returning Values

·         Lambda Functions

·         Object-Oriented Concepts

·         Standard Libraries

·         Modules Used in Python

·         The Import Statements

·         Module Search Path

·         Package Installation Ways

·         Errors and Exception Handling

·         Handling Multiple Exceptions

  Lecture 16-22 Introduction To Statistics
Live Lecture 
·      Introduction To Statistic

·      Distributions and Hypothesis Tests

·      Distributions of One Variable

·      Hypothesis Testing

·      Typical Analysis Procedure

·      Data Screening and Outliers

·      Normality Check

·      Hypothesis Concept, Errors, p-Value, and Sample Size

·      Chi Square

·      ANOVA

·      Practical Exercise

  Lecture 23-24 Pandas
Live Lecture 
·      Pandas Introduction

·      Series

·      Data Frames

·      CSVs

·      Data from URLs

·      Describing Data with Pandas

·      Selecting and Viewing Data with Pandas

·      Manipulating Data

·      Practical Exercise

  Lecture 25-27 NumPy
Live Lecture 
·      Mathematical Computing with Python (NumPy)

·      NumPy Introduction

·      NumPy DataTypes and Attributes

·      Creating NumPy Arrays

·      NumPy Random Seed

·      Viewing Arrays and Matrices

·      Manipulating Arrays

·      Standard Deviation and Variance

·      Reshape and Transpose

·      Dot Product vs Element Wise

·      Store Sales

·      Comparison Operators

·      Sorting Arrays

·      Turn Images Into NumPy Arrays

·      Practical Exercise

  Lecture-28 Introduction to Machine Learning
Live Lecture 
·      What Is Machine Learning?

·      AI/Machine Learning/Data Science

·      Practical Exercise

  Lecture 29-30 Machine Learning and Data Science Framework
Live Lecture 
·      Machine Learning Framework

·      Types of Machine Learning

·      Types of Data

·      Types of Evaluation

·      Features In Data

·      Modelling - Splitting Data

·      Modelling - Picking the Model

·      Modelling - Tuning

·      Modelling – Comparison

·      Practical Exercise

  Lecture-31 Data Science Environment Setup
Live Lecture 
·      Introducing Our Tools

·      Windows Environment Setup

·      Linux Environment Setup

·      Jupyter Notebook Walkthrough

·      Practical Exercise

  Lecture 32-35 Matplotlib + Seaborn: Plotting and Data Visualization
Live Lecture 
·      Matplotlib Introduction

·      Importing And Using Matplotlib

·      Anatomy Of A Matplotlib Figure

·      Scatter Plot And Bar Plot

·      Histograms And Subplots

·      Subplots Option 2

·      Plotting From Pandas DataFrames

·      Customizing Your Plots

·      Saving And Sharing Your Plots

·      Practical Exercise

  Lecture 36-40 Scikit-learn: Creating Machine Learning Models
Live Lecture 
·      Scikit-learn Introduction

·      Scikit-learn Cheatsheet

·      Typical scikit-learn Workflow

·      Debugging Warnings In Jupyter

·      Splitting Your Data

·      Clean, Transform, Reduce

·      Convert Data To Numbers

·      Handling Missing Values With Pandas

·      Handling Missing Values With Scikit-learn

·      Choosing The Right Model For Your Data

·      Practical Exercise

  Lecture 41-45 Scikit-learn - Regression Model
Live Lecture 
·      Types of Regression Algorithms

·      Simple Linear Regression

·      Multiple Linear Regression

·      Logistic Regression

·      Polynomial Regression

·      Support Vector Regression

·      Ridge Regression

·      Lasso Regression

·      ElasticNet Regression

·      Bayesian Regression

·      Decision Tree Regression

·      Random Forest Regression

·      Practical Exercise

  Lecture 46-48 Scikit-learn – Classification
Live Lecture 
·      Types of Classification Algorithms

·      Logistic Regression/Classification

·      K-Nearest Neighbours

·      Support Vector Machines

·      Kernel Support Vector Machines

·      Naive Bayes

·      Decision Tree Classification

·      Random Forest Classification

·      Practical Exercise

  Lecture 49-50 K Means Clustering
Live Lecture 
·      K-Means Clustering

·      A Simple Example of Clustering

·      Clustering Categorical Data

·      How to Choose the Number of Clusters

·      Pros and Cons of K-Means Clustering

·      To Standardize or not to Standardize

·      Relationship between Clustering and Regression

·      Market Segmentation with Cluster Analysis

·      Species Segmentation with Cluster Analysis

·      Advanced Statistical Methods - Other Types of Clustering

·      Types of Clustering

·      Dendrogram

·      Heatmaps

·      Practical Exercise

  Lecture 51-53 Text Mining
Live Lecture 
·      The concepts of text-mining

·      Use cases

·      Text Mining Algorithms

·      Quantifying text

·      TF-IDF

·      Beyond TF-IDF

·      Practical Exercise

  Lecture 54-58 Basic Time Series Forecasting
Live Lecture 
·      What is time series?

·      Techniques and applications

·      Time series components

·      Moving average

·      Smoothing techniques

·      Exponential smoothing

·      Univariate time series models

·      Multivariate time series analysis

·      Sentiment analysis in Python (Twitter sentiment analysis)

·      Text analysis

·      Rolling Mean For Detecting Temporal Variation

·      Simple Exponential Smoothing (SES)

·      Holt extended simple exponential smoothing

·      Holt Winters

·      Auto Regression Model (AR): Consider Previous Time Steps

·      Implement a Basic ARIMA Model

·      Automated ARIMA & Account for Seasonality (SARIMA)

·      Practical Exercise

  Lecture 59-60 Principal Component Analysis (PCA)
Live Lecture 
·      Primer

·      Measurement

·      Assumptions

·      Applied PCA work flow

·      Analysis of performance

·      Dimensionality reduction with (PCA)

·      Practical Exercise

  Case Studies
Live 
·      Case Study -1 Creating Application- oriented Analyses Using Tax Data

·      Preparing for the analysis of top incomes

·      Importing and exploring the world's top incomes dataset

·      Analyzing and visualizing the top income data of the India

·      Furthering the analysis of the top income groups of the India

·      Reporting with Jinja2                    

·      Case Study -2 Driving Visual Analyses with Automobile Data

·      Preparing to analyze automobile fuel efficiencies

·      Exploring and describing fuel efficiency data with Python

·      Analyzing automobile fuel efficiency over time with Python

·      Investigating the makes and models of automobiles with Python                       

·      Case Study -3 Working with Social Graphs

·      Preparing to work with social networks in Python

·      Importing networks

·      Exploring subgraphs within a heroic network

·      Finding strong ties

·      Finding key players

·      Exploring characteristics of entire networks

·      Clustering and community detection in social networks

·      Visualizing graphs             

·      Case Study -4 Recommending Movies at Scale

·      Modeling preference expressions

·      Understanding the data

·      Ingesting the movie review data

·      Finding the highest-scoring movies

·      Improving the movie-rating system

·      Measuring the distance between users in the preference space

·      Computing the correlation between users

·      Finding the best critic for a user

·      Predicting movie ratings for users

·      Collaboratively filtering item by item

·      Building a nonnegative matrix factorization model

·      Loading the entire dataset into the memory

·      Dumping the SVD-based model to the disk

·      Training the SVD-based model                

·      Case Study -5 Har vesting and Geo locating Twitter Data

·      Creating a Twitter application

·      Understanding the Twitter API v1.1

·      Determining your Twitter followers and friends

·      Pulling Twitter user profiles

·      Making requests without running afoul of Twitter's rate limits

·      Storing JSON data to the disk

·      Setting up MongoDB for storing the Twitter data

·      Storing user profiles in MongoDB using PyMongo

·      Exploring the geographic information available in profiles

·      Plotting geospatial data in Python            

·      Case Study -6 Optimizing Numerical Code with NumPy and Scipy

·      Understanding the optimization process

·      Identifying common performance bottlenecks in code

·      Reading through the code

·      Profiling Python code with the Unix time function

·      Profiling Python code using built-in Python functions

·      Profiling Python code using IPython's %timeit function

·      Profiling Python code using line_profiler

·      Plucking the low-hanging (optimization) fruit

·      Testing the performance benefits of NumPy

·      Rewriting simple functions with NumPy

·      Optimizing the innermost loop with NumPy

Frequently Asked Questions

The candidates willing to join the introduction to machine learning training should have a prior acquaintance on fundamentals of of programming & matrix algebra.

The course offers a variety of online training options, including: Live Virtual Classroom Training: Participate in real-time interactive sessions with instructors and peers. 1:1 Doubt Resolution Sessions: Get personalized assistance and clarification on course-related queries. Recorded Live Lectures*: Access recorded sessions for review or to catch up on missed classes. Flexible Schedule: Enjoy the flexibility to learn at your own pace and according to your schedule.

Live Virtual Classroom Training allows you to attend instructor-led sessions in real-time through an online platform. You can interact with the instructor, ask questions, participate in discussions, and collaborate with fellow learners, simulating the experience of a traditional classroom setting from the comfort of your own space.

If you miss a live session, you can access recorded lectures* to review the content covered during the session. This allows you to catch up on any missed material at your own pace and ensures that you don't fall behind in your learning journey.

The course offers a flexible schedule, allowing you to learn at times that suit you best. Whether you have other commitments or prefer to study during specific hours, the course structure accommodates your needs, enabling you to balance your learning with other responsibilities effectively. *Note: Availability of recorded live lectures may vary depending on the course and training provider.