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  • Understand AI and Machine Learning in detail
  • Understand Data Preprocessing
  • Define Supervised Learning
  • Describe Feature Engineering
  • Identify the Classifications of Supervised Learning
  • Define Unsupervised Learning
  • Understand Time Series Modeling
  • Describe Ensemble Learning
  • Explain Recommender Systems
  • Understand Text Mining

About the Course:

The “Machine Learning” course is an intermediate level course, curated exclusively for both beginners and professionals. The course covers the basics as well as the advanced level concepts. The course contains content based videos along with practical demonstrations, that performs and explains each step required to complete the task.

 

Learning Objectives:

 

By the end of the course, you will be able to learn about:

  • Evolution of Artificial Intelligence

  • Sci-Fi Movies with the Concept of AI

  • Recommender Systems

  • Relationship between Artificial Intelligence, Machine Learning, and Data Science

  • Definition and Features of Machine Learning

  • Machine Learning Approaches

  • Machine Learning Techniques

  • Applications of Machine Learning

  • Data Exploration Loading Files

  • Importing and Storing Data

  • Data Exploration Techniques

  • Seaborn

  • Correlation Analysis

  • Data Wrangling

  • Missing Values in a Dataset

  • Outlier Values in a Dataset

  • Outlier and Missing Value Treatment

  • Data Manipulation

  • Functionalities of Data Object in Python

  • Different Types of Joins

  • Typecasting

  • Labor Hours Comparison

  • Introduction to Supervised Learning

  • Example of Supervised Learning

  • Understanding the Algorithm

  • Supervised Learning Flow

  • Types of Supervised Learning

  • Types of Classification Algorithms

  • Types of Regression Algorithms

  • Regression Use Case

  • Accuracy Metrics

  • Cost Function

  • Evaluating Coefficients

  • Linear Regression

  • Challenges in Prediction

  • Types of Regression Algorithms

  • Bigmart

  • Logistic Regression

  • Sigmoid Probability

  • Accuracy Matrix

  • Survival of Titanic Passengers

  • Feature Selection

  • Principal Component Analysis (PCA)

  • Eigenvalues and PCA

  • Linear Discriminant Analysis

  • Overview of Classification

  • Use Cases of Classification

  • Classification Algorithms

  • Decision Tree Classifier

  • Decision Tree Examples

  • Decision Tree Formation

  • Choosing the Classifier

  • Overfitting of Decision Trees

  • Random Forest Classifier- Bagging and Bootstrapping

  • Decision Tree and Random Forest Classifier

  • Performance Measures: Confusion Matrix

  • Performance Measures: Cost Matrix

  • Naive Bayes Classifier

  • Support Vector Machines : Linear Separability

  • Support Vector Machines : Classification Margin

  • Non-linear SVMs

  • Overview of unsupervised learning

  • Example and Applications of Unsupervised Learning

  • Introduction to Clustering

  • K-means Clustering

  • Optimal Number of Clusters

  • Cluster Based Incentivization

  • Overview of Time Series Modeling

  • Time Series Pattern Types

  • White Noise

  • Stationarity

  • Removal of Non-Stationarity

  • Air Passengers

  • Beer Production

  • Time Series Models

  • Steps in Time Series Forecasting

  • Overview of Ensemble Learning

  • Ensemble Learning Methods

  • Working of AdaBoost

  • AdaBoost Algorithm and Flowchart

  • Gradient Boosting

  • Introduction to XGBoost

  • Parameters of XGBoost

  • Pima Indians Diabetes

  • Model Selection

  • Common Splitting Strategies

  • Cross Validation

  • Introduction to recommender system

  • Purposes of Recommender Systems

  • Paradigms of Recommender Systems

  • Collaborative Filtering

  • Association Rule Mining

  • Association Rule Mining: Market Basket Analysis

  • Association Rule Generation: Apriori Algorithm

  • Apriori Algorithm Example

  • Apriori Algorithm: Rule Selection

  • User-Movie Recommendation Model

  • Introduction to text mining

  • Need of Text Mining

  • Applications of Text Mining

  • Natural Language ToolKit Library

  • Text Extraction and Preprocessing: Tokenization

  • Text Extraction and Preprocessing: N-grams

  • Text Extraction and Preprocessing: Stop Word Removal

  • Text Extraction and Preprocessing: Stemming

  • Text Extraction and Preprocessing: Lemmatization

  • Text Extraction and Preprocessing: POS Tagging

  • Text Extraction and Preprocessing: Named Entity Recognition

  • NLP Process Workflow

  • Wiki Corpus

...and much more!

If you're new to this technology, don't worry - the course covers the topics from the basics. If you've done some programming before, you should pick it up quickly.

If you’re a programmer looking to switch into an exciting new career track, this course will teach you the basic techniques used by real-world industry Machine Learning developers. These are topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now!

  • No prerequisites are required, as the course covers the concepts from the scratch. However, basic knowledge of Python would help.
  • Python developers curious about Machine Leaning
  • Candidates who are willing to learn Machine Learning from scratch
  • Python developers willing to upskill themselves
  • Data Scientist willing to upskill themselves
  • IT professional willing to switch their career in Machine Learning
View More...
  • Section 1 : Machine Learning 14 Lectures 01:10:59

    • Lecture 1 :
    • Overview of Machine Learning Preview
    • Lecture 2 :
    • Data Preprocessing
    • Lecture 3 :
    • Demo: Data Preprocessing
    • Lecture 4 :
    • Supervised Learning
    • Lecture 5 :
    • Demo: Regression
    • Lecture 6 :
    • Feature Engineering
    • Lecture 7 :
    • Demo: Feature Engineering
    • Lecture 8 :
    • Supervised Learning Classification
    • Lecture 9 :
    • Demo: Classification
    • Lecture 10 :
    • Unsupervised Learning
    • Lecture 11 :
    • Time Series Modeling
    • Lecture 12 :
    • Ensemble Learning
    • Lecture 13 :
    • Recommender Systems
    • Lecture 14 :
    • Text Mining
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    Yes. All of the courses comes with online video based lectures created by certified instructors. Instructors have crafted these courses with a blend of high quality interactive videos, lectures, quizzes & real world projects to give you an indepth knowledge about the topic.
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    Yes absolutely & thats one of the advantage of self-paced courses. You can anytime pause or resume the course & come back & forth from one lecture to another lecture, play the videos mulitple times & so on.
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  • Is there any free trial?

    Currently, we do not offer any free trial.
  • Can i cancel anytime?

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Skillcart is an e-learning company that helps learners upskill in different technology domains with its customized courses that can be used by both beginners and professionals. Skillcart provides self-paced e-learning videos for all types of technical courses. Skillcart has courses available for Artificial Intelligence, Data Science with Python, Machine Learning, Deep Learning, and many more.
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