Description

An excellent choice for beginners and professionals looking to expand their knowledge on Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Supervised and Unsupervised Learning.

 

This is an introductory course to enhance your knowledgeThis course gives introduction to to AI, Machine Learning, Data Science, Deep Learning, Supervised and Unsupervised learning with real time examples where machine learning can be applied to solve or simplify real world business problems.

 

Radiant Techlearning offers “AI & ML Training Program” training program in Classroom & Virtual Instructor Led / Online mode.

Pre-requisites

  • None

Course Content

Day 1: Introduction to Python Programming Language

  • Basics of Python
  • Python-1 (defining variables, if else loop, for loop, while loop)
  • Python-2 (functions, functions with parameter, predefined functions)
  • Python-3 (Classes, inheritance)
  • Python-4 (OOPS concepts)
  • Python-5 (Exception Handling)
  • Python-6 (I/O Operations)

Day 2: Feature Engineering

  • What is Machine Learning?
  • Differences between AI,ML and Deep Learning
  • What is Data Science
  • Numpy -1(Array creation, manipulation of arrays)
  • Numpy -2 (Creating Multidimensional array)
  • Numpy -3 (Indexing and slicing operation)
  • Numpy-4 (inbuilt functions in numpy)
  • Pandas- 1(Introduction to Pandas and uses of pandas library)
  • Pandas-2(Discussion on Series, creating Series and its inbuilt functions)
  • Pandas-3(Creating Dataframes, Dataframes inbuilt function)
  • Pandas-4(Reading different types of files such as csv,excel,html and json using
  • Pandas)
  • Matplotlib (Visualizing data in various forms)
  • Data Cleaning-1(Data preprocessing techniques)
  • Data Cleaning-2(Data preprocessing techniques)
  • Feature Enginering and Data preprocessing use cases for large dataset

Day 3: Supervised Machine Learning Techniques

Regression Techniques

  • Simple Linear Regression Algorithm
  • Multiple Regression Algorithm
  • Polynomial Regression
  • Evaluating Regression Model Performance

Classification Techniques

  • Logistic Regression
  • K- Nearest Neighbor
  • Decision Tree Classification
  • Random Forest Classification

Day 4

  • XGboost
  • Dimensionality Reduction
  • Principal Component Analysis
  • Evaluating Classification Model Performance

Unsupervised Machine Learning technique

Clustering Techniques

  • K-Means Clustering
  • Hierarchical Clustering

Model Selection

  • K Fold Cross Validation
  • Grid Search

USE CASES- Included in all the above topic

Day-5

  • Natural Language Processing
  • Text Analysis, Text Pre-processing
  • Bag of Words,TF-IDF model
  • Naïve Baye’s Theorem
  • Time Series Data
  • Arima Model

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