Artificial Intelligence & Machine Learning

The global artificial intelligence market is valued at USD 39.9 billion in 2019 and is expected to grow at a compound annual growth rate (CAGR) of 42.2% from 2020 to 2027.

  • Duration : 8 Weekend
  • English


Artificial Intelligence & Machine Learning
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Course Overview

We have specially designed a customized Industrial Training curriculum on Artificial Intelligence (AI) & Machine Learning (ML) for working professionals and job seekers to meet industry requirements. This course aims to get the future technology knowledge and to acquire hands-on exposure in Artificial Intelligence and Machine Language. The faculties will experiment all kinds of problems specific to logical solutions with AI & ML Technology.

The global artificial intelligence market is valued at USD 39.9 billion in 2019 and is expected to grow at a compound annual growth rate (CAGR) of 42.2% from 2020 to 2027. The continuous research and innovation directed by the tech giants are driving the adoption of advanced technologies in industry verticals, such as automotive, healthcare, retail, finance, and manufacturing. However, technology has always been an essential element for these industries, but AI has brought technology at the Centre of the organizations.


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Course Content

  • Lecture: 01
    Environment Setup
  • Lecture: 02
    Variable Types and Basic Operators
  • Lecture: 03
    Decision Making and Loops
  • Lecture: 04
    Strings
  • Lecture: 05
    Lists
  • Lecture: 06
    Tuples
  • Lecture: 07
    Dictionary
  • Lecture: 08
    Functions
  • Lecture: 09
    Classes and Objects
  • Lecture: 01
    Introduction and Environment
  • Lecture: 02
    Ndarray Object
  • Lecture: 03
    Data Types
  • Lecture: 04
    Array Attributes and Creation Routines
  • Lecture: 05
    Indexing & Slicing
  • Lecture: 06
    Advanced Indexing
  • Lecture: 07
    Iterating Over Array
  • Lecture: 08
    Histogram Using Matplotlib
  • Lecture: 01
    Introduction and Installation
  • Lecture: 02
    Pyplot API
  • Lecture: 03
    Simple Plot
  • Lecture: 04
    PyLab module
  • Lecture: 05
    Axes Class
  • Lecture: 06
    Subplots() Function
  • Lecture: 07
    Bar Plot
  • Lecture: 08
    Histogram
  • Lecture: 01
    Introduction and Environment Setup
  • Lecture: 02
    Introduction to Data Structures
  • Lecture: 03
    Series
  • Lecture: 04
    DataFrame
  • Lecture: 05
    Panel
  • Lecture: 06
    Basic Functionality
  • Lecture: 07
    Categorical Data and Visualization
  • Lecture: 08
    Read/Write-Excel, Text, CSV
  • Lecture: 01
    Introduction and Installation
  • Lecture: 02
    Steps to implement
  • Lecture: 03
    Introduction & Installation
  • Lecture: 04
    Steps to implement
  • Lecture: 05
    Introduction and Installation
  • Lecture: 06
    Steps to implement
  • Lecture: 01
    Mean, Median, Mode, Variance
  • Lecture: 02
    Correlation, Regression
  • Lecture: 03
    Hypothesis testing, Kurtosis
  • Lecture: 04
    Skewness
  • Lecture: 05
    Percentiles and Outliers
  • Lecture: 06
    Normal Distribution
  • Lecture: 07
    Vectors
  • Lecture: 08
    Scalar, matrices
  • Lecture: 09
    Tensor
  • Lecture: 10
    Eigenvalues and eigenvectors
  • Lecture: 01
    Training Data
  • Lecture: 02
    Test Data
  • Lecture: 03
    Fitting Data
  • Lecture: 04
    Loss function
  • Lecture: 05
    Optimization
  • Lecture: 06
    Metrics
  • Lecture: 01
    Regression (Supervised Learning)
  • Lecture: 02
    Linear Regression (Supervised Learning)
  • Lecture: 03
    Polynomial Regression (Supervised Learning)
  • Lecture: 04
    Classification (Supervised Learning)
  • Lecture: 05
    Logistic Regression (Supervised Learning)
  • Lecture: 06
    SVM, KNN (Supervised Learning)
  • Lecture: 07
    Decision Tree, Random Forest (Supervised Learning)
  • Lecture: 08
    Clustering (Unsupervised Learning)
  • Lecture: 09
    K-Means clustering (Unsupervised Learning)
  • Lecture: 10
    Dimensionality reduction (Unsupervised Learning)
  • Lecture: 11
    Principle Component Analysis (PCA) (Unsupervised Learning)
  • Lecture: 12
    Yarowsky algorithm (Semi supervisor Learning)
  • Lecture: 13
    Self-training classifier(Semi supervisor Learning)
  • Lecture: 14
    Reinforcement learning Framework (Reinforcement learning)
  • Lecture: 15
    Types of RL Systems (Reinforcement learning)
  • Lecture: 16
    Q-learning (Reinforcement learning)
  • Lecture: 01
    Fundamental of Neural Networks
  • Lecture: 02
    Single –Layer Perceptron (SLP)
  • Lecture: 03
    Multi –Layer Perceptron (MLP)
  • Lecture: 04
    Feed Forward Networks (FFN)
  • Lecture: 05
    Convolutional Neural Networks (CNN)
  • Lecture: 06
    Back-Propagation Networks (BPN)
  • Lecture: 07
    Recurrent Neural Networks (RNN)

Course Orientation Video

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Batch Details

Training Duration: 8 Weekends

New batch starts soon!

Who can Attend


  • Professionals or Job Seekers who want to up-skill in AI & ML
  • Working Professional
  • S/W Professional 
  • IT Job Seeker
  • People with Basic Program Knowledge
  • Graduates ( EEE, ECE, CSE, IT, Machetronics, BCA, MCA, BSc, MSc )

Pre-requisites

  • PC/ Laptop with Internet Connection
  • Basic Knowledge in any Programming Language

Training Outcome

  • You can be in trend with the latest technology and its tools.
  • You can apply the basic principles, models, and algorithms of AI to recognize, model, and solve problems in the analysis and design of information systems.
  • You can analyse the structures and algorithms of a selection of techniques related to searching, reasoning, machine learning, and language processing.
  • You can review research articles from well-known journals and conference proceedings regarding the theories and applications of AI.
  • You can carry out a research project and write a research proposal, report and paper.

CourseCertificate

Skills Covered

  • Python
  • Essential Statistics
  • Essential Mathematics
  • Data Processing
  • Machine Learning

Learning path


Tools Used

Skilltechnika's Advantage

Live Interaction

Live Interaction Session

Hands-On Practice

Hands On Practice

Real Time Project

Real Time Project

Curriculum

Latest Curriculum

Recorded Session

Recorded Sessions

Industry Connects

Industry Connects

Peer Learning

Peer Learning

Mentor Support

Mentor Support

Assessments

Assessments

Training Options

Live Online Session
18000
QUICK ENROLLMENT
18000
GROUP ENROLLMENT
Customized to your team's needs

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