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Machine Learning Certification Using Python (Virtual Instructor-led Training)

> Data Scientist has been named the best job in America for 2018 with a median base salary of $242,000 and 4,524 job openings - Forbes

> Roles like Chief Data Scientist & Chief Analytics Officers have emerged to ensure that analytical insights drive business strategies - Forbes

> Businesses Will Need One Million Data Scientists by 2020 – Kdnuggets

USD 449 USD 649

Course Overview

Machine Learning Certification Training using Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning.

Key Highlights

  • 36 Hours of Online Live Instructor-led Classes
  • Weekend class : 12 sessions of 3 hours each
  • Weekday class : 18 sessions of 2 hours
  • Real-life Case Studies
  • Practical assignments
  • Lifetime access to Learning Management System (LMS)
  • 24 x 7 Expert Support
  • Certification
  • Global community forum for all our users
  • No exam included
  • Industry based Projects
  • No exam Included 

What You'll Learn

  • Era of Data Science
  • Business Intelligence vs Data Science
  • Data Extraction, Wrangling, & Visualization
  • Introduction to Machine Learning with Python
  • Dimensionality Reduction
  • Reinforcement Learning
  • Time Series Analysis

SCHEDULE

    • Delivery Format: Virtual Classroom Live
    • Location: Online
    • Access Period: 6 Weeks
    • Course Date: NOV 06 th
    • Course Time: 09:30 PM to 12:30 AM (EDT)
    • Session: Weekdays
    • Total Class: FRI & SAT (12 Sessions)
    QTY
    USD 449 USD 649
    • Delivery Format: Virtual Classroom Live
    • Location: Online
    • Access Period: 6 Weeks
    • Course Date: DEC 05 th
    • Course Time: 11:00 AM to 02:00 PM (EDT)
    • Session: Weekend
    • Total Class: SAT & SUN (12 Sessions)
    QTY
    USD 449 USD 649

Career Benefits

  • Opportunity to work in multiple industries, such as, automotive, e-commerce, social media, etc
  • Be considered an expert of data pre-processing, model evaluation, and dimensional reduction
  • Scope of developing Machine Learning Applications
  • Options to carry out hands-on data analysis using Python
  • Higher paycheck

Who Can Attend

  • Developers with plans of transition in Data Science
  • Analytics Professionals
  • Information Architects
  • Business Analysts
  • Python Professionals
  • Fresh Graduates who want to enter Data Science

Exam Formats

No exam Included.

Course Delivery

This course is available in the following formats:

  • Virtual Classroom Live Duration: 36 Hrs

Related Courses

Course Syllabus


Introduction to Data Science

Goal: Get an introduction to Data Science in this Module and see how Data Science helps to analyze large and unstructured data with different tools.

 

Objectives: At the end of this Module, you should be able to:

  • Define Data Science
  • Discuss the era of Data Science
  • Describe the Role of a Data Scientist
  • Illustrate the Life cycle of Data Science
  • List the Tools used in Data Science
  • State what role Big Data and Hadoop, Python, R and Machine Learning play in Data Science

 

Topics:

  • 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 Python

Data Extraction, Wrangling, & Visualization

Goal: Discuss the different sources available to extract data, arrange the data in structured form, analyze the data, and represent the data in a graphical format.

 

Objectives: At the end of this Module, you should be able to:

  • Discuss Data Acquisition techniques
  • List the different types of Data
  • Evaluate Input Data
  • Explain the Data Wrangling techniques
  • Discuss Data Exploration

 

Topics:

  • Data Analysis Pipeline
  • What is Data Extraction
  • Types of Data
  • Raw and Processed Data
  • Data Wrangling
  • Exploratory Data Analysis
  • Visualization of Data

 

Hands-On/Demo:

  • Loading different types of dataset in Python
  • Arranging the data
  • Plotting the graphs

Introduction to Machine Learning with Python

Goal: In this module, you will learn the concept of Machine Learning and it’s types.

 

Objective: At the end of this module, you should be able to:

  • Essential Python Revision
  • Necessary Machine Learning Python libraries
  • Define Machine Learning
  • Discuss Machine Learning Use cases
  • List the categories of Machine Learning
  • Illustrate Supervised Learning Algorithms
  • Identify and recognize machine learning algorithms around us
  • Understand the various elements of machine learning algorithm like parameters, hyper parameters, loss function and optimization.

 

Topics:

  • Python Revision (numpy, Pandas, scikit learn, matplotlib)
  • What is Machine Learning?
  • Machine Learning Use-Cases
  • Machine Learning Process Flow
  • Machine Learning Categories
  • Linear regression
  • Gradient descent

Supervised Learning - I

Goal: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.

 

Objective: At the end of this module, you should be able to:

  • Understand What is Supervised Learning?
  • Illustrate Logistic Regression
  • Define Classification
  • Explain different Types of Classifiers such as Decision Tree and Random Forest

 

Topics:

  • 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?

 

Hands-On:

  • Implementation of Logistic regression, Decision tree, Random forest

Dimensionality Reduction

Goal: In this module you will learn about impact of dimensions within data. You will be taught to perform factor analysis using PCA and compress dimensions. Also, you will be developing LDA model.  

 

Objective: At the end of this module, you should be able to:

  • Define the importance of Dimensions
  • Explore PCA and its implementation
  • Discuss LDA and its implementation

 

Topics:

  • Introduction to Dimensionality
  • Why Dimensionality Reduction
  • PCA
  • Factor Analysis
  • Scaling dimensional model
  • LDA

 

Hands-On:

  • PCA
  • Scaling

Supervised Learning - II

Goal: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.

 

Objective: At the end of this module, you should be able to:

  • Understand What is Naïve Bayes Classifier
  • How Naïve Bayes Classifier works?
  • Understand Support Vector Machine
  • Illustrate How Support Vector Machine works?
  • Hyperparameter optimization

 

Topics:

  • What is Naïve Bayes?
  • How Naïve Bayes works?
  • Implementing Naïve Bayes Classifier
  • What is Support Vector Machine?
  • Illustrate how Support Vector Machine works?
  • Hyperparameter optimization
  • Grid Search vs Random Search
  • Implementation of Support Vector Machine for Classification

 

Hands-On:

  • Implementation of Naïve Bayes, SVM

Unsupervised Learning

Goal: In this module, you will learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.

 

Objective: At the end of this module, you should be able to:

              o    K - means Clustering 

              o    C - means Clustering 

              o    Hierarchical Clustering

  • Define Unsupervised Learning
  • Discuss the following Cluster Analysis

 

Topics:

  • What is Clustering & its Use Cases?
  • What is K-means Clustering?
  • How K-means algorithm works?
  • How to do optimal clustering
  • What is C-means Clustering?
  • What is Hierarchical Clustering?
  • How Hierarchical Clustering works?

 

Hands-On:

  • Implementing K-means Clustering
  • Implementing Hierarchical Clustering

Association Rules Mining and Recommendation Systems

Goal: In this module, you will learn Association rules and their extension towards recommendation engines with Apriori algorithm.

 

Objective: At the end of this module, you should be able to:

  • Define Association Rules
  • Learn the backend of recommendation engines and develop your own using python

 

Topics:

  • What are Association Rules?
  • Association Rule Parameters
  • Calculating Association Rule Parameters
  • Recommendation Engines
  • How Recommendation Engines work?
  • Collaborative Filtering
  • Content Based Filtering

 

Hands-On:

  • Apriori Algorithm
  • Market Basket Analysis

Reinforcement Learning

Goal: In this module, you will learn about developing a smart learning algorithm such that the learning becomes more and more accurate as time passes by. You will be able to define an optimal solution for an agent based on agent environment interaction.

 

Objective: At the end of this module, you should be able to

  • Explain the concept of Reinforcement Learning
  • Generalize a problem using Reinforcement Learning
  • Explain Markov’s Decision Process
  • Demonstrate Q Learning

 

Topics:

  • What is Reinforcement Learning
  • Why Reinforcement Learning
  • Elements of Reinforcement Learning
  • Exploration vs Exploitation dilemma
  • Epsilon Greedy Algorithm
  • Markov Decision Process (MDP)
  • Q values and V values
  • Q – Learning
  • ? values

Time Series Analysis

Goal: In this module, you will learn about Time Series Analysis to forecast dependent variables based on time. You will be taught different models for time series modelling such that you analyse a real time dependent data for forecasting.


Objective: At the end of this module, you should be able to:

  • Explain Time Series Analysis (TSA)
  • Discuss the need of TSA
  • Describe ARIMA modelling
  • Forecast the time series model

 

Topics:

  • What is Time Series Analysis?
  • Importance of TSA
  • Components of TSA
  • White Noise
  • AR model
  • MA model
  • ARMA model
  • ARIMA model
  • Stationarity
  • ACF & PACF

 

Hands-On:

  • Checking Stationarity
  • Converting a non-stationary data to stationary
  • Implementing Dickey Fuller Test
  • Plot ACF and PACF
  • Generating the ARIMA plot
  • TSA Forecasting

Model Selection and Boosting

Goal: In this module, you will learn about selecting one model over another. Also, you will learn about Boosting and its importance in Machine Learning. You will learn on how to convert weaker algorithms to stronger ones.

 

Objective: At the end of this module, you should be able to:

  • Discuss Model Selection
  • Define Boosting
  • Express the need of Boosting
  • Explain the working of Boosting algorithm

 

Topics:

  • What is Model Selection?
  • Need of Model Selection
  • Cross – Validation
  • What is Boosting?
  • How Boosting Algorithms work?
  • Types of Boosting Algorithms
  • Adaptive Boosting

 

Hands-On:

  • Cross Validation
  • AdaBoost

In-Class Project

Goal: In this module, you will learn how to approach and implement a Project end to end, and a Subject Matter Expert will share his experience and insights from the industry to help you kickstart your career in this domain. Finally, we will be having a Q&A and doubt clearing session.

 

Objectives: At the end of this module, you should be able to:

  • How to approach a project
  • Hands-On project implementation
  • What Industry expects
  • Industry insights for the Machine Learning domain
  • QA and Doubt Clearing Session

FAQ's


What if I miss a class?

You will never miss a lecture at Upskill Yourself! You can choose either of the two options:

  • View the recorded session of the class available in your LMS.
  • You can attend the missed session, in any other live batch.

Can I attend a demo session before enrollment?

We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in a class.

Mike Williams, Direct Consultant