Machine Learning Training Program

Enroll in Zeblearns Machine Learning Training Program to delve into the core concepts and techniques of machine learning. Explore supervised and unsupervised learning algorithms, regression, classification, clustering, and neural networks. Gain hands-on experience through practical exercises and real-world projects. Prepare for industry-recognized certifications in machine learning. Equip yourself with the skills needed to leverage machine learning for data-driven insights and decision-making. Register now for comprehensive training with Zeblearn.

Course content

About Machine Learning

Join Our Online Classroom!

Exercise: Meet Your Classmates & Instructor

Asking Questions + Getting Help

What Is Machine Learning?

ZTM Resources

Exercise: Machine Learning Playground

How Did We Get Here?

Exercise: YouTube Recommendation Engine

Types of Machine Learning

Are You Getting It Yet?

What Is Machine Learning? Round 2

Section Review

Monthly Coding Challenges, Free Resources, and Guides

Section Overview

Introducing Our Framework

Types of Machine Learning Problems

Types of Data

Types of Evaluation

Features In Data

Modelling - Splitting Data

Modelling - Picking the Model

Modelling - Tuning

Modelling - Comparison

Overfitting and Underfitting Definitions

Experimentation

Tools We Will Use

Optional: Elements of AI

What is Conda?

Conda Environments

Mac Environment Setup

Mac Environment Setup 2

Windows Environment Setup

Windows Environment Setup 2

Linux Environment Setup

Sharing your Conda Environment

Jupyter Notebook Walkthrough

Section Overview

Downloading Workbooks and Assignments

Pandas Introduction

Series, Data Frames and CSVs

Data from URLs

Quick Note: Upcoming Videos

Describing Data with Pandas

Selecting and Viewing Data with Pandas

Quick Note: Upcoming Videos

Selecting and Viewing Data with Pandas Part 2

Manipulating Data

Manipulating Data 2

Manipulating Data 3

Assignment: Pandas Practice

How To Download The Course Assignments

NumPy Introduction

Quick Note: Correction In Next Video

NumPy DataTypes and Attributes

Creating NumPy Arrays

NumPy Random Seed

Viewing Arrays and Matrices

Manipulating Arrays

Manipulating Arrays 2

Standard Deviation and Variance

Reshape and Transpose

Dot Product vs Element Wise

Exercise: Nut Butter Store Sales

Comparison Operators

Sorting Arrays

Turn Images Into NumPy Arrays

Exercise: Imposter Syndrome

Assignment: NumPy Practice

Optional: Extra NumPy resources

Matplotlib Introduction

Importing And Using Matplotlib

Anatomy Of A Matplotlib Figure

Scatter Plot And Bar Plot

Histograms And Subplots

Subplots Option 2

Quick Tip: Data Visualizations

Plotting From Pandas DataFrames

Quick Note: Regular Expressions

Plotting From Pandas DataFrames

Scikit-learn Introduction

Quick Note: Upcoming Video

Refresher: What Is Machine Learning?

Quick Note: Upcoming Videos

Typical scikit-learn Workflow

Optional: Debugging Warnings In Jupyter

Getting Your Data Ready: Splitting Your Data

Quick Tip: Clean, Transform, Reduce

Getting Your Data Ready: Convert Data To Numbers

Note: Update to next video (OneHotEncoder can handle NaN/None values)

Getting Your Data Ready: Handling Missing Values With Pandas

Extension: Feature Scaling

Note: Correction in the upcoming video (splitting data)

Getting Your Data Ready: Handling Missing Values With Scikit-learn

NEW: Choosing The Right Model For Your Data

NEW: Choosing The Right Model For Your Data 2 (Regression)

Quick Note: Decision Trees

Quick Tip: How ML Algorithms Work

Choosing The Right Model For Your Data 3 (Classification)

Fitting A Model To The Data

Making Predictions With Our Model

predict() vs predict_proba()

NEW: Making Predictions With Our Model (Regression)

NEW: Evaluating A Machine Learning Model (Score) Part 1

NEW: Evaluating A Machine Learning Model (Score) Part 2

Evaluating A Machine Learning Model 2 (Cross Validation)

Evaluating A Classification Model 1 (Accuracy)

Evaluating A Classification Model 2 (ROC Curve)

Evaluating A Classification Model 3 (ROC Curve)

Reading Extension: ROC Curve + AUC

Evaluating A Classification Model 4 (Confusion Matrix)

NEW: Evaluating A Classification Model 5 (Confusion Matrix)

Evaluating A Classification Model 6 (Classification Report)

NEW: Evaluating A Regression Model 1 (R2 Score)

NEW: Evaluating A Regression Model 2 (MAE)

NEW: Evaluating A Regression Model 3 (MSE)

Machine Learning Model Evaluation

NEW: Evaluating A Model With Cross Validation and Scoring Parameter

NEW: Evaluating A Model With Scikit-learn Functions

Improving A Machine Learning Model

Tuning Hyperparameters

Tuning Hyperparameters 2

Tuning Hyperparameters 3

Note: Metric Comparison Improvement

Quick Tip: Correlation Analysis

Saving And Loading A Model

Saving And Loading A Model 2

Putting It All Together

Data Engineering Introduction

What Is Data?

What Is A Data Engineer?

What Is A Data Engineer 3?

What Is A Data Engineer 4?

Types Of Databases

Quick Note: Upcoming Video

Optional: OLTP Databases

Optional: Learn SQL

Hadoop, HDFS and MapReduce

Apache Spark and Apache Flink

Kafka and Stream Processing

Deep Learning and Unstructured Data

Setting Up With Google

Setting Up Google Colab

Google Colab Workspace

Uploading Project Data

Setting Up Our Data

Setting Up Our Data 2

Importing TensorFlow 2

Optional: TensorFlow 2.0 Default Issue

Using A GPU

Optional: GPU and Google Colab

Optional: Reloading Colab Notebook

Loading Our Data Labels

Preparing The Images

Turning Data Labels Into Numbers

Creating Our Own Validation Set

Preprocess Images

Preprocess Images 2

Turning Data Into Batches

Turning Data Into Batches 2

Visualizing Our Data

Preparing Our Inputs and Outputs

Optional: How machines learn and what's going on behind the scenes?

Building A Deep Learning Model

Building A Deep Learning Model 2

Building A Deep Learning Model 3

Building A Deep Learning Model 4

Summarizing Our Model

Evaluating Our Model

Preventing Overfitting

Training Your Deep Neural Network

Evaluating Performance With TensorBoard

Make And Transform Predictions

Transform Predictions To Text

Visualizing Model Predictions

Visualizing And Evaluate Model Predictions 2

Visualizing And Evaluate Model Predictions 3

Saving And Loading A Trained Model

Training Model On Full Dataset

Making Predictions On Test Images

Submitting Model to Kaggle

Finishing Dog Vision: Where to next?

What Is A Programming Language

Python Interpreter

How To Run Python Code

Latest Version Of Python

Our First Python Program

Python 2 vs Python 3

Exercise: How Does Python Work?

Learning Python

Python Data Types

How To Succeed

Numbers

Math Functions

DEVELOPER FUNDAMENTALS: I

Operator Precedence

Exercise: Operator Precedence

Optional: bin() and complex

Variables

Expressions vs Statements

Augmented Assignment Operator

Strings

String Concatenation

Type Conversion

Escape Sequences

Formatted Strings

String Indexes

Immutability

Built-In Functions + Methods

Booleans

Exercise: Type Conversion

DEVELOPER FUNDAMENTALS: II

Exercise: Password Checker

Lists

List Slicing

Matrix

List Methods

List Methods 2

List Methods 3

Common List Patterns

List Unpacking

None

Dictionaries

DEVELOPER FUNDAMENTALS: III

Dictionary Keys

Dictionary Methods

Dictionary Methods 2

Tuples

Tuples 2

Sets

Why Should You Learn Machine Learning Training?

The annual salary of an Machine Learning is $125k.

Request more information

TRAINING FEATURES

Instructor-led Sessions

Engage in live, interactive sessions led by industry experts, ensuring better guidance, monitoring, and flexible learning from any internet-enabled device.

Real-life Case Studies

Gain practical insights through case studies based on real-world industry applications, helping bridge the gap between theory and practice.

Assignments

Enhance your analytical skills and understanding with hands-on assignments designed to reinforce key concepts and practical application.

Lifetime Access

Enjoy unrestricted access to course materials for a lifetime, allowing you to learn and revisit topics at your own pace.

24/7 Expert Support

Get round-the-clock assistance from experienced mentors to resolve your queries and enhance your learning experience.

Certification

Earn industry-recognized certification upon course completion, validating your skills and boosting your career prospects.

Track

Week Days

Course Duration

Fast Track

Week Days

40-45 Days

2 Hrs. Per Day

Online

Course Duration

7 Weekends

3 Hrs. Per Day

Online

Fast Track

8 Days

6+ Hrs. Per Day

Online

Students Reviews

Explore authentic student feedback on ZebLearn’s expert-led SAP training and foreign language courses. Discover success stories from learners who earned certifications, enhanced their skills, and advanced their careers!

Rohit Gupta

Data Scientist

The Machine Learning course at Zeblearn India has been a game-changer for my career. The comprehensive curriculum covered all the essential algorithms and techniques, and the practical projects provided hands-on experience that was invaluable.

Amit Verma

Software Engineer

Zeblearn Indias Machine Learning course provided a perfect balance of theory and practice. The real-world case studies and assignments helped me apply machine learning concepts effectively. This course has opened up new opportunities for me in the field of AI.

Sneha Mehta

AI Specialist

I am extremely satisfied with the Machine Learning course offered by Zeblearn India. The instructors were knowledgeable and approachable, and the course content was well-structured and thorough. The skills I gained have significantly enhanced my ability to develop AI solutions.

Rajesh Singh

Research Engineer

Taking the Machine Learning course from Zeblearn India was a fantastic experience. The instructors were experts in their field, and the course content was up-to-date and relevant. The hands-on projects were particularly beneficial in reinforcing my learning.

Neha Kapoor

Data Analyst

The Machine Learning course at Zeblearn India was an excellent investment in my career. The detailed explanations and interactive sessions made complex topics easier to understand. The course has equipped me with the skills needed to build and deploy machine learning models successfully.

Date

Type

Schedule

Time

SOLD OUT

23 June 2025

Weekend

SAT - SUN (08 Week)

18:00 To 20:00

28 June 2025

Weekday

MON - FRI (08 Week)

08:00 To 10:00

3 July 2025

Weekend

MON - FRI (08 Week)

10:00 To 00:00

Machine Learning Training Program

Check Your Course Fee

KEY POINTS

90+

18+

1500+

5+

ZebLearn Certification Partners – Globally Recognized Credentials!

Course content

Why Should You Learn Machine Learning Training?

Request more information

What you will get at Zeblearnindia Learning?

TRAINING FEATURES

Instructor-led Sessions

Real-life Case Studies

Assignments

Lifetime Access

24/7 Expert Support

Certification

We're Here to Help!

Phone Support

WhatsApp Support

(23 June ) 60 Minutes WEBINAR with CERTIFICATE

Students Reviews

Rohit Gupta

Amit Verma

Sneha Mehta

Rajesh Singh

Neha Kapoor