Artificial Intelligence and Machine Learning with Python Training

Artificial Intelligence and Machine Learning with Python Training introduces the Python-based methods used to build intelligent, data-driven applications. The course explores data preparation, supervised and unsupervised learning, model evaluation, and core AI concepts through practical Python workflows. Learners work with algorithms for prediction, classification, clustering, and pattern recognition while understanding how machine learning models are developed and applied.

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Curriculum

Artificial Intelligence And Machine Learning With Python Training Course Syllabus Structure

Artificial Intelligence And Machine Learning With Python Training curriculum blends theory with code‑first labs. Learners complete 25 mini‑projects covering regression, classification, and neural networks.

Define Python

Overview of Python

Understand why Python is Popular

Setup Python Environment

Understand Operands and Expressions

The Companies using Python

Different Applications where Python is Used

Discuss Python Scripts on UNIX/Windows

Values, Types, Variables

Operands and Expressions

Conditional Statements

Loops

Command Line Arguments

Writing to the Screen

Write your First Python Program

Understand Command Line Parameters and Flow Control

Python files I/O Functions

Numbers

Strings and related operations

Tuple - properties, related operations, compared with list

List - properties, related operations

Dictionary - properties, related operations

Set – properties

Understand Python Standard Libraries

Packages and Module - Modules, Import Options, sys Path

Functions - Syntax, Arguments, Keyword Arguments, Return Values

Function Parameters

Global Variables

Variable Scope and Returning Values

Lambda - Features, Syntax, Options, Compared with the Functions

Sorting - Sequences, Dictionaries, Limitations of Sorting

Errors and Exceptions - Types of Issues, Remediation

Object Oriented Concepts

Modules Used in Python

The Import Statements

Module Search Path

Package Installation Ways

Errors and Exception Handling

Handling Multiple Exceptions

Create arrays using NumPy

Perform various operations on arrays and manipulate them

Indexing, slicing, and iterating

Read & write data from text/CSV files into arrays and vice-versa

Create Series and Data Frames in Pandas

Data structures & index operations in Pandas

Importing and exporting data

Indexing and slicing of data structures in Pandas

Reading and writing data from Excel/CSV formats into Pandas

Create simple plots in matplotlib

Grids, axes, plots, markers, colors, fonts, and styling

Types of plots: bar graphs, pie charts, histograms, contour plots

Choose the right plot format for a problem at hand judiciously

Scale and add style to your plots

Basic functionalities of a data object

Merging of data objects

Concatenation of data objects

Types of joins on data objects

Exploring a dataset

Analyzing a dataset

Need of machine learning

Introduction to machine learning

Types of machine learning: supervised, unsupervised, and reinforcement learning

Machine learning with Python

Applications of machine learning

Introduction to supervised learning and its types (regression and classification)

Introduction to regression

Simple linear regression

Multiple linear regression and assumptions in linear regression

Math behind linear regression

Introduction to classification

Linear regression vs logistic regression

Math behind logistic regression: detailed formulas, the logit function and odds

Confusion matrix and accuracy

True positive rate, false positive rate

Threshold evaluation with ROCR

Introduction to tree-based classification

Understanding a decision tree: impurity function, entropy, and information gain for node splitting

Concepts of information gain, impurity function, Gini index, overfitting, pruning, pre-pruning, post-pruning, and cost-complexity pruning

Introduction to ensemble techniques: bagging, random forests, and determining the optimal number of trees in a random forest

Introduction to probabilistic classifiers

Understanding Naïve Bayes and the math behind Bayes' theorem

Understanding Support Vector Machines (SVM)

Kernel functions in SVM and the math behind SVM in Python with ML

Types of unsupervised learning, such as clustering and dimensionality reduction

Introduction to k-means clustering

Math behind k-means clustering

Dimensionality reduction with PCA

Introduction to Natural Language Processing (NLP)

Introduction to text mining

Importance and applications of text mining

How NLP works with text mining

Writing and reading word files

Language Toolkit (NLTK) environment

Text mining: Cleaning, pre-processing, and text classification

Introduction to Natural Language Processing (NLP)

Introduction to text mining Python with ML

Importance and applications of text mining

How NLP works with text mining

Writing and reading word files

Language Toolkit (NLTK) environment

Text mining: Cleaning, pre-processing, and text classification

Introduction to Deep Learning with neural networks

Biological neural networks vs artificial neural networks

Understanding perception learning algorithm

Introduction to Deep Learning frameworks

TensorFlow constants, variables, and placeholders

What is time series? Its techniques and applications

Time series components

Moving average, smoothing techniques, and exponential smoothing

Univariate time series models

Multivariate time series analysis

ARIMA model and time series in Python

Sentiment analysis in Python (Twitter sentiment analysis) and text analysis

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Status	Date	Type	Schedule	Time
SOLD OUT	30 Aug 2026	Weekend	SAT - SUN (08 Weeks)	18:00 - 20:00
FILLING FAST	4 Sep 2026	Weekday	MON - FRI (08 Weeks)	08:00 - 10:00
AVAILABLE	9 Sep 2026	Weekend	SAT - SUN (08 Weeks)	10:00 - 12:00

Status

Date

Type

Schedule

Time

SOLD OUT

30 Aug 2026

Weekend

SAT - SUN (08 Weeks)

18:00 - 20:00

FILLING FAST

4 Sep 2026

Weekday

MON - FRI (08 Weeks)

08:00 - 10:00

AVAILABLE

9 Sep 2026

Weekend

SAT - SUN (08 Weeks)

10:00 - 12:00

FAQ

Frequently Asked Questions

Q: What core Python libraries are taught in the Artificial Intelligence And Machine Learning With Python Training? A: The training covers NumPy, pandas, scikit‑learn, TensorFlow, and PyTorch for AI and ML tasks. Q: How does the Artificial Intelligence And Machine Learning With Python Training evaluate model performance? A: Students learn to use accuracy, precision, recall, F1‑score, and ROC‑AUC metrics on validation datasets. Q: Can the Artificial Intelligence And Machine Learning With Python Training be applied to real‑world data projects? A: The curriculum includes a capstone project that processes a publicly available dataset to build and deploy a predictive model.

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Artificial Intelligence and Machine Learning with Python Training

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Artificial Intelligence And Machine Learning With Python Training Course Syllabus Structure

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Artificial Intelligence and Machine Learning with Python Training

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Zeblearn Certification Partners

Artificial Intelligence And Machine Learning With Python Training Course Syllabus Structure

Introduction to Python

Sequences and File Operations

Deep Dive – Functions, OOPs, Modules, Errors and Exceptions

Introduction to NumPy, Pandas and Matplotlib

Data Manipulation using MySQL

Introduction to Machine Learning

Supervised Learning and Linear Regression

Classification and Logistic Regression

Decision Tree and Random Forest

Naïve Bayes and Support Vector Machine (self-paced)

Unsupervised Learning

Natural Language Processing and Text Mining (self-paced)

Natural Language Processing and Text Mining (self-paced)

Introduction to Deep Learning

Time Series Analysis (self-paced)

Beyond Learning

FREE ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING WITH PYTHON TRAINING SELF ASSESSMENT

Hear From Our Certified Learners

Feedback from Our Students

What You Get at Zeblearn

Get exclusive access to career resources upon completion

Evaluation & Practice Sessions

Learning Simulation (LMS)

Career & Placement Assistance

60-Minute Free Webinar with Certificate

Upcoming Live Classes

Artificial Intelligence and Machine Learning with Python Training — Flexible Batches For You

Frequently Asked Questions

1 −

2 +

3 +

SAP FICO
(Finance & Controlling)

SAP MM
(Materials Management)

SAP SD
(Sales & Distribution)

SAP PP
(Production Planning)

SAP HCM
(Human Capital Mgmt)

SAP BW
(Business Warehouse)

SAP S/4HANA
(Finance)

SAP QM
(Quality Management)

SAP PM
(Plant Maintenance)

SAP EWM
(Extended Warehouse)

SAP CRM
(Customer Relations)

FREE
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING WITH PYTHON TRAINING
SELF ASSESSMENT