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Program Overview

This program provides comprehensive knowledge of most popular and extensively used Machine learning & Artificial Intelligence techniques through self paced videos and instructor led live online sessions. In this program, you will learn about the machine learning solutions like recommender systems, natural language processing, deep learning & computer vision. Using the skills acquired in the course you will be ready to take on real world challenges and build Machine Learning & AI solutions. You will also master python, the most widely used and popular language. You will be able to use python not only for general purpose programming but also for data analysis.

Machine Learning & Artificial Intelligence with Python

PROGRAM FEATURES

3 Months Program
  • 22 Hours of self paced video based learning
  • 9 Case Studies
  • 8 Hours of sectional quizzes
  • 6 X 2 Hours Instructor led Live online sessions
KEY FEATURES
  • 12 months access to LEAP*
  • Quick Support from industry experts
  • Hands-on knowledge of Building Solutions
PREREQUISITES
  • Programming Experience: Not mandatory but good to have
  • Familiarity with statistical and algorithmic aspects is a plus.

PROGRAM STRUCTURE

Learn at your own pace

From Basics to Professional
The series of online self-paced learning give a holistic overview right from basics concepts of coding to building AI solutions.

Interact with the trainer

Fortnightly Live Online Sessions
We understand self-learning is not enough! Hence, we provide live interactions with experts and faculties every fortnight for 3- months filled with value additions and realtime doubt clarifications.p>

Withing 24 hours query resolution

Ask away your doubts anytime
We will connect you with industry experts, faculties and fellow peers. Ask your doubts and we promise expert reply within 24 hours with added flavor of learning from peers.

Self-Paced Learning Overview

Introduction to Python - 4Hrs

  • Installing Python – Anaconda & Pip
  • Learn about the basics of Python: Data type, Variable, objects
  • Learn about conditional statements and loops
  • Learn how to create user defined functions
  • Learn how to handles different types of files
  • Learn about databases and how to interface them with Python
  • Learn about SQLite
  • Learn about important python libraries for data analysis: Pandas, NumPy

Introduction to Machine Learning – 8 Hrs

  • Introduction to Machine Learning
  • Learn about different techniques of Machine Learning and how to evaluate them
  • Learn about popular supervised learning techniques for classification and regression: Logistic regression, NB, KNN, SVM, ANN, Decision Tree, Random Forest, Linear Regression
  • Learn about popular unsupervised machine learning techniques like: Clustering and Dimensionality Reduction Techniques, Association Learning Rule
  • Learn about overfitting and techniques to overcome it

Introduction to Deep Learning – 1.5 Hrs

  • Learn about Perceptron
  • Learn about Artificial Neural Network
  • Learn about computer vision

Introduction to Recommender Systems – 3.5 Hrs

  • Learn about recommender system
  • Learn about popular techniques for recommender system
    • Collaborative Filtering
    • Content Based Filtering
    • Latent Factor Collaborative Filtering

Introduction to NLP with Python – 6 Hrs

  • Introduction to Regex
  • Introduction to Wordnet
  • Learn about python toolkit for NLP
  • Learn about basics like TF-IDF Metric
  • Learn about Text Classification
  • Learn about web scraping using beautiful soup
  • Learn about Auto summarization
  • Learn about Sentiment Analysis

Live Online Sessions Overview

How to improve your Machine Learning model?

Learn about techniques like

  • Feature Engineering
  • Hyper parameter tuning

Advanced Regression techniques

  • Polynomial regression
  • Ridge, Lasso and Elastic Net Regression

Computer Vision:

IntroConvolutional duction to Neural Network

Computer Vision:

Introduction to Object Detection – Case study

Advance Natural Language Processing:

Topic modelling with Latent Dirichlet Allocation

Advance AI:

Implement a Chatbot in Python – Case Study

1000
Training Hours
559
Students & Alumini
13
Collaborations
4.7
Avg Feedback (out of 5)

Course Contents

Introduction to Python
Learning Outcome: To be able to master python for general purpose programming
  • Introduction to Coding: This module introduces Python and refer coding to cooking. Also it gives a brief about Anaconda and Pip as Anaconda package contains the required tools that you will need for exploring machine learning.
  • Data types: This module covers the most common built in types i.e. Lists and what all things can be done in lists.
  • Variables: Variables are nothing but reserved memory locations to store values. This module covers the basics of variables-what are variables, types of variables and how variables are just like containers.
  • Conditional Statements: This module gives a brief about conditional statements like if-else, dictionaries- how they are used , what are the properties.
  • Loops: What are loops, types of loops and how do we iterate values in loops etc.- All these topics are being covered in this module.
  • Functions: This module describes functions as food processors, passing arguments in a function and what is recursion and how recursive functions work.
  • An Object oriented state of mind: What is a class, and how a class is an object of species, the importance of objects and class and a "is a" inheritance concept
  • Our first series of program: This is an interesting module as it gets the learner started to code on how to download a webpage and how to handle the errors that occur while executing a program.
  • Files: What is a file? How to download and unzip a file , parsing a csv file through few packages and few other topics are being covered in the below module.
  • Database: This module starts with an interesting example of implementing a Bank ATM through code then walks through database management system and how you can put it to use.
  • Introduction to Python Libraries
 
 
Introduction to Machine Learning
Learning Outcome: To be able to grasp essentials of machine learning and related algorithms and equip you improve learning from data without human intervention
Duration: 8 Hours
  • Introduction to Machine Learning: This module takes the learner through the overview of Machine Learning program and provides a brief introduction to the course.
  • Machine Learning for Spam Detection: This module gives a brief about the spam detection problem, what algorithms can be used to solve spam detection and types of ML problems.
  • Classification Problems: The different algorithms that can be used for classification problems are being covered in this module, for e.g. Naïve Bayes, KNN, SVM, ANN etc. It covers case study of survival prediction in titanic case study covering different algorithms for classification problems.
  • Clustering: The module describes what is clustering, what are the methods of clustering. But it mainly focuses on K-Means Clustering and also walks through a data clustering algorithm-DBSCAN.
  • Association Rule Mining: This module gives an introduction of Association Rule learning and how it is being used in many industries especially ecommerce.
  • Dimensionality Reduction: Sometimes data comes in a very jumbled way and its difficult to sort it out, dimensionality reduction comes to the rescue. This module covers how it helps in clean the data and technique used for dimensionality reduction.
  • Regression as a form of supervised learning: The module walks the learner through an example of Demand forecasting through linear and logistic regression. Also it explains about the trade off that a learner needs to keep in mind while training the dataset in supervised learning. It also covers an interesting Melbourne pricing case study.
  • Decision Trees: This module covers the titanic case study and will use ensembling and random forest techniques. It will also cover the most common problem of Machine Learning
  • Random Forests: The module explains what are random forests? How they are different from Decision Trees? Describing Bagging and Bootstrap sampling and cross validating random forest and decision trees in the titanic case study.
  • Problem of overfitting: This module covers the problem of overfitting and the ensemble learning techniques like bagging, boosting and stacking.

Supported with Hands-on exercises, quizzes and assignments

NLP with Python
Learning Outcome: To be able to successfully deploy the sentiment analysis model from tweets about a brand or person
Duration: 6 Hours
  • Natural Language Processing: This module will make use of our basic Machine Learning knowledge put to use. It will explain Natural Language Process starting with Rule based approach.  It will also cover case study of auto summarization text using Natural Language Toolkit, web scraping news article through different algorithms.
  • Sentiment Analysis: The module explains what is Sentiment Analysis and why it is important nowadays, Using different approaches for Sentiment Analysis, defining sentiment lexicons to figure out the features to be used in sentiment analysis through Twitter sentiment analysis case study.

Supported with case studies and quizzes.

Recommender Systems
Learning Outcome: To be able to successfully be able to give recommendations based on the online feedback and reviews
Duration: 3.5 Hours
  • Recommendation Systems: While you surf Netflix or you are shopping on amazon, you must have noticed an amazing feature showing you some recommended picks which may turn out to be of your use. Now how do they know what to recommend to whom. This is called as recommendation systems. This module will cover the challenges and intricacies behind these systems and implement movie recommendation system case study.
  • Recommendation Techniques: Collaborative Filtering, Content Based Filtering, Latent Factor Collaborative Filtering

Supported with case studies and quizzes.

Deep Learning
Learning Outcome: To be able to successfully deploy the handwritten digit recognition model
  • Deep Learning and Computer Vision: This module provides a brief overview of some of the most significant deep learning scheme used in computer vision problem such as handwriting recognition, face detection etc. It also covers a case study of handwritten digit recognition from MNIST database.

Supported with case studies and quizzes.

FAQs

What is hybrid learning program?

It is a unique blend of Intructor led live online sessions (live virtual sessions ) and self paced sessions giving the participants a holistic way of learning driving them towards making an impact.

How would machine learning and artificial intelligence hybrid learning program would help me in career growth?

This program provides comprehensive knowledge of most popular and extensively used Machine learning & Artificial Intelligence techniques through self paced videos and instructor led live online sessions. In this program, you will learn about the machine learning solutions like recommender systems, natural language processing, deep learning & computer vision. Using the skills acquired in the course you will be ready to take on real world challenges and build Machine Learning & AI solutions. You will also master python, the most widely used and popular language. You will be able to use python not only for general purpose programming but also for data analysis.

What would be the structure of the intructor led live sessions?

Intructor LED Live Sessions would be connected zoom meetings of 2 hours spanned over 3 months with fortnightly sessions. However to speedup your journey you may attend all the sessions in lesser time as we are conducting all of the sessions every month.

What will I learn from this training?

You will gain intelligence on:

  • Learn basics of python programming
  • The most unsupervised algorithms from Bagging, Boosting and Random Forest along with regression to KNN and decision tree techniques.
  • Efficient use of Panda and Numpy for process the input data into the user data. You can accomplish the data mining tasks, etc.
  • Building Neural Networks to represent the most advanced reinforcement learning.
  • Tactfully using the technology to get accurate result on building the UI of an application using for data visualization and other big data models and a lot more.
Why to study Machine Learning with Python?

Nowadays, this is listed amongst the most demanded skill-sets around the globe. Whether you are a software developer, product manager, or a business analyst learning machine learning with Python can take your professional life to the next level. The training has been designed to provide you not only rigorous and practical, but concise as well. In other words, this training is the first step for achieving your goals.

Whom do I contact if I want to know more about this training?

You can write us at info@scholarspro.com or you can also contact us on the number provided on the website (relevant for your country).

CERTIFICATION

Get a Completion Certificate post earning 80% score in quizzes and evaluations.


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