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Machine Learning Master Program

Our Machine learning Master's Program online training course includes training on the latest advancements and technical approaches in Machine Learning such as Graphical Models and Reinforcement Learning.

  • 35000
  • 40000
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  • Course Includes
  • Live Class Practical Oriented Training
  • 120 + Hrs Instructor LED Training
  • 50 + Hrs Practical Exercise
  • 35 + Hrs Project Work & Assignment
  • Timely Doubt Resolution
  • Dedicated Student Success Mentor
  • Certification & Job Assistance
  • Free Access to Workshop & Webinar
  • No Cost EMI Option
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What you will learn

  • Understand how to make an accurate predictions
  • Learn how to deal with the advanced techniques like Dimensionality Reduction
  • Develop an understanding on the issues of specific topics like Reinforcement Learning, NLP and Deep Learning & how to ha...
  • Develop understanding of many of the Machine Learning models
  • Develop understanding on all the essentials such as: Data Preprocessing, Regression, Classification, Clustering, Associa...
  • Determine the various applications of machine learning algorithms
  • Learn the how to implement the unsupervised learning algorithms, which includes deep learning, clustering, and recommen...

Requirements

  • Basic understanding of Computer Programming Languages.

Description

|| About Machine Learning Master Program Course

Machine Learning Master’s Program Online Training Course aims to insight the candidates on the Data Preprocessing, Clustering: K-Means, Hierarchical Clustering, Reinforcement Learning: Upper Confidence Bound, Thompson Sampling, Dimensionality Reduction: PCA, LDA, Kernel PCA, Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost, Reinforcement Learning: Upper Confidence Bound, Thompson Sampling, Deep Learning: Artificial Neural Networks, Convolutional Neural Networks, etc. Machine Learning is basically the process to collect real-world data, extract useful information from it, and then take actions to perform certain tasks without manual programming. It helps systems improve over time on their own by exploring various types of real-world data. It also allows organizations to improve their business strategies by knowing the insights that are extracted from the given business data.

 

All these helps the candidates in building their career as a ML Engineer professional, who can create a strong added value to your business for sure.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 analyzing the data set with ease. In the training modules algorithms such as: regression, clustering, classification,  and recommendation have been introduced, all these helps the candidates in supervising the advanced data programing techniques.

Course Content

Lecture-1 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

·      Practical Exercise              

Lecture-2 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

·      Practical Exercise              

Lecture-3 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

·      Practical Exercise              

Lecture-4 Introduction to Data Science for Python

·      What is Data Science?

·      History of Data Science

·      Methodologies

·      Data Science Applications

·      Image Recognition

·      Speech Recognition

·      Business Intelligence vs. Data Science

·      Data Science Life-Cycle

·      Practical Exercise              

Lecture-5 Python Data Science Environment Setup

·      Install Python

·      Getting Anaconda for Data Science Environment Setup

·      Anaconda Navigator

·      Installing Anaconda

·      Install Miniconda

·      Setting up a Virtual Environment

·      Important Python Data Science Packages

·      How to Get Jupyter Notebook?

·      Practical Exercise              

Lecture-6 Python Data Cleansing by Pandas & Numpy

·      Python Data Cleansing – Prerequisites

·      Python Data Cleansing Operations on Data using NumPy

·      Python Data Cleansing Operations on Data Using pandas

·      Dataframe

·      Panel

·      Series

·      Python Data Cleansing

·      Ways to Cleanse Missing Data in Python

·      Python Data Cleansing – Other Operations

·      Practical Exercise              

Lecture-7 Python Data File Formats

·      How to Read CSV, JSON, and XLS Files

·      Python Data File Formats

·      Prerequisites

·      Read CSV File in Python

·      Read JSON File in Python

·      Practical Exercise              

Lecture-8 Working with Relational Database with Python

·      Introduction

·      Prerequisites for Relational Database

·      Reading a Relational Table

·      Insert Values in Relational Database with Python

·      Delete Values in Relational Database with Python

·      Practical Exercise              

Lecture-9 Work with NoSQL Database in Python using PyMongo

·      What is NoSQL Database?

·      Need for NoSQL Database in Python

·      Database Types with NoSQL

·      Document Databases

·      Graph Stores

·      Key-Value Stores

·      Wide-Column Stores

·      Benefits of Using NoSQL Database

·      NoSQL vs. SQL

·      Installing the Prerequisites of NoSQL Database in Python

·      Operations Perform in NoSQL Database in Python

·      Practical Exercise              

Lecture-10 Python Stemming and Lemmatization

·      Prerequisites for Python Stemming and Lemmatization

·      Python Stemming

·      Python Lemmatization

·      Practical Exercise              

Lecture-11 Aggregation and Data Wrangling with Python

·      DataFrames

·      Python Data Wrangling – Prerequisites

·      Why we need Data Wrangling with Python

·      Dropping Missing Values

·      Grouping Data

·      Filtering Data

·      Pivoting Dataset

·      Melting Shifted Datasets

·      Merging Melted Data

·      Reducing into an ABT

·      Concatenating Data

·      Exporting Data

·      How Python Aggregate Data?

·      Practical Exercise              

Lecture-12 Python Statistics

·      Introduction

·      p-value in Python Statistics

·      T-test in Python Statistics

·      KS Test in Python Statistics

·      Correlation in Python Statistics

·      Practical Exercise              

Lecture-13 Python Descriptive Statistics

·      Data Analysis

·      Descriptive Statistics in Python

·      Central Tendency in Python

·      Dispersion in Python

·      Pandas with Descriptive Statistics in Python

·      Practical Exercise              

Lecture-14 Python Probability Distributions

·      What is Python Probability Distribution?

·      Implement Python Probability Distributions

·      Normal Distribution in Python

·      Binomial Distribution in Python

·      Poisson Distribution in Python

·      Bernoulli Distribution in Python

·      Practical Exercise              

Lecture-15 Introduction to Python Anaconda

·      What is Anaconda?

·      Benefits of Using Python Anaconda

·      Python Anaconda Installation

·      Installing Python Anaconda Libraries

·      Anaconda Navigator

·      Practical Exercise              

Lecture-16 Python Matplotlib

·      What is Python Matplotlib?

·      Python Matplotlib – Pyplot

·      Python Matplotlib Keyword Strings

·      Categorical Variables to Python Plotting

·      Some Line Properties of Matplotlib

·      Showing a Grid in Python Plot

·      Practical Exercise              

Lecture-17 Python Scatter Plot & Python BoxPlot

·      What is Python Scatter & BoxPlot?

·      Create Python BoxPlot Using Matplotlib

·      Create a Python Scatter Plot

·      Practical Exercise              

Lecture-18 Python Charts

·      Prerequisites for Python Charts

·      Bubble Charts

·      3D Charts

·      Python Charts Properties

·      Styling your Python Chart

·      How to Save Python Charts File?

·      Practical Exercise              

Lecture-19 Python Heatmap and Word Cloud

·      What is Python Heatmap & Word Cloud?

·      Create a Heatmap in Python

·      Normalizing a column

·      Create a Word Cloud Python

·      Practical Exercise              

Lecture-20 Python Histogram and Python Bar Plot

·      Introduction to Python Histogram

·      Displaying Histogram, Rug, and Kernel Density

·      Customizing the rug

·      Customizing the density distribution

·      Vertical Python Histogram

·      Python Histogram with multiple variables

·      Introduction to Python Bar Plot

·      Horizontal Python Bar Plot

·      Adding Title and Axis Labels

·      Practical Exercise              

Lecture-21 Geographic Maps & Graph Data

·      Prerequisites for Python Geographic Maps and Graph Data

·      Python Geographic Maps

·      Python Graph Data

·      Sparse graphs

·      Practical Exercise              

Lecture-22 Python Time Series Analysis

·      What is Time Series in Python?

·      Plotting a Python Histogram

·      Plotting a Density Plot in Python Time Series

·      Autocorrelation Plot in Python Time Series

·      Plotting a Lag Plot in Python Time Series

·      Practical Exercise              

Lecture-23 Python Linear Regression

·      What is Python Linear Regression?

·      Chi-Square Test

·      Practical Exercise              

Lecture-24 Introduction to Machine Learning with Python

·      Supervised Learning

·      Unsupervised Learning

·      Steps in Python Machine Learning

·      Applications of Python Machine Learning

·      Practical Exercise              

Lecture-25 Environment Setup and Installation Process

·      How to Install Python?

·      Starting and Updating Anaconda

·      Installing Needed Python Libraries

·      Practical Exercise              

Lecture-26 Data Pre-processing, Analysis & Visualization

·      Data Pre-processing in Python Machine Learning

·      Python Data Pre-processing Techniques

·      Analyzing Data in Python Machine Learning

·      Visualizing Data-Univariate Plots in Python Machine Learning

·      Visualizing Data-Multivariate Plots in Python Machine Learning

·      Practical Exercise              

Lecture-27 Train and Test Set

·      Training and Test Data in Python Machine Learning

·      How to Split Train and Test Set in Python Machine Learning?

·      Plotting of Train and Test Set in Python

·      Practical Exercise              

Lecture-28 Machine Learning Techniques with Python

·      Machine Learning Techniques vs. Algorithms

·      Machine Learning Regression

·      Linear Regression and Non-Linear Regression

·      Machine Learning Classification

·      Decision Tree Induction

·      Rule-based Classification

·      Classification by Back propagation

·      Lazy Learners

·      Clustering

·      Anomaly Detection

·      Practical Exercise              

Lecture-29 Machine Learning Algorithms in Python

·      Linear Regression

·      Logistic Regression

·      Decision Tree

·      Support Vector Machines (SVM)

·      Naive Bayes

·      kNN (k-Nearest Neighbors)

·      Random Forest

·      Practical Exercise              

Lecture-30 Introduction to Deep Learning with Python

·      What is Deep Learning with Python?

·      Characteristics of Deep Learning With Python

·      Deep Neural Networks

·      Deep Learning Applications

·      Practical Exercise              

Fees

Offline Training @ Vadodara

  • Classroom Based Training
  • Practical Based Training
  • No Cost EMI Option
45000 40000

Online Training preferred

  • Live Virtual Classroom Training
  • 1:1 Doubt Resolution Sessions
  • Recorded Live Lectures*
  • Flexible Schedule
40000 35000

Corporate Training

  • Customized Learning
  • Onsite Based Corporate Training
  • Online Corporate Training
  • Certified Corporate Training

Certification

  • Upon the completion of the Classroom training, you will have an Offline exam that will help you prepare for the Professional certification exam and score top marks. The BIT Certification is awarded upon successfully completing an offline exam after reviewed by experts
  • Upon the completion of the training, you will have an online exam that will help you prepare for the Professional certification exam and score top marks. BIT Certification is awarded upon successfully completing an online exam after reviewed by experts.
  • This course is designed to solve different Machine Learning Case studies on various Domains using Python. This certificate is very well recognized over 80 top MNCs from around the world and some of the Fortune 500 companies.

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