Advance Python for AI & ML

The tremendous potentialities that AI & ML has are interesting. Dive deep and build great intuitive tools, develop models and use powerful algorithms to solve problems

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Advanced Python for AI & ML training program overview

This course offers practical and task-oriented training on PyCharm, a cross-platform Integrated Development Environment (IDE) for Python. You will learn the basics of python i.e the variables, operations, functions, loops, classes, and objects. Also, learn how to install TensorFlow and various other machine learning algorithms.

Overview

Skills

You will be able to build Artificial Intelligence projects based on deep learning.

Overview

Outcome

At the end of this course, you will learn how the various Artificial Intelligence and Machine Learning techniques are applied in the industry.

The registration process

Once you have completed our simplified enrolment process, you’ll receive an email confirmation with your payment receipt in your registered email ID. You can then access the entire content of the online student portal immediately by logging in to your account on our site. Should you require any assistance please reach out to us via email (support@greycampus.com) or via our online chat system.

The course curriculum

The curriculum for Advanced Python for AI & ML training course covers following list of broad topics.

  • Pycharm Files
  • Downloading and Installing
  • Pycharm and Python
  • Exploring the Pycharm Interface
  • Variables
  • Variables Operations and
  • Conversions
  • Collection Types
  • Collections Operations
  • Control Flow If Statements
  • While and For Loops
  • Functions
  • Classes and Objects
  • Header
  • Panda read csv
  • datatype and statistics
  • Panda column operations
  • Panda operations
  • Merge and concat
  • Tables
  • Graphs
  • One Dimension
  • Two Dimension
  • Two Dimension stacking
  • Installing TensorFlow
  • Importing Tensorflow to Pycharm
  • Constant Nodes and Sessions
  • Variable Nodes
  • Placeholder Nodes
  • Operation nodes
  • Loss, Optimizers, and Training
  • Building a Linear Regression Model
  • Setting up Prebuilt Estimator Model
  • Evaluating and Predicting with
  • Prebuilt Model
  • Building Custom Estimator
  • Function
  • Testing the Custom Estimator
  • Function
  • Model Comparison
  • Linear Regression
  • Logistic Regression
  • Decision Tree
  • SVM
  • Naive Bayes
  • KNN
  • K-Means
  • Random Forest
  • Regression
  • Linear Regression
  • Logistic Regression
  • What is linear regression?
  • The advertising dataset
  • Simple Linear Regression
  • Hypothesis testing and p-values
  • R squared
  • Multiple linear regression
  • Model and feature selection
  • Model evaluation
  • Handling categorical features
  • Predicting a continuous response
  • Quick refresher on linear regression
  • Predicting a categorical response
  • Using logistic regression
  • Probability, odds, log-odds
  • What is logistic regression?
  • Interpreting logistic regression
  • Using logistic regression with categorical features