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AI & ML – CodeChef Certified

A 6-month immersive program in Artificial Intelligence and Machine Learning, designed in collaboration with CodeChef. Covers core AI/ML concepts, real-world use cases, and 12 industry-grade projects. Ideal for developers and professionals looking to break into the AI industry.

5
(1 rating)
Course Instructor: Academy Admin

₹50000.00

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Course Overview

Schedule of Classes

Course Curriculum

1 Subject

AI/ML (MAL)

126 Learning Materials

Module - 1 : Python Foundation : SET 1

PYTHON AND PYCHARM INSTALLATION

Video
00:26:11

INTRODUCTION TO PYTHON

Video
01:01:09

IDENTIFIERS, KEYWORD, DATATYPES AND OPERATORS

Video
01:04:20

OPERATORS

Video
01:01:45

CONDITIONAL STATEMENTS

Video
00:34:04

Slot Booking

External Link

SET 2

MULTIPLE CONDITIONS AND SIMPLE CALCULATOR

Video
00:16:00

MULTIPLE CONDITIONS

Video
00:05:19

LOOPING - FOR LOOPS

Video
01:00:50

FOR LOOP - QUESTIONS

Video
00:37:47

FOR AND WHILE LOOP

Video
01:00:21

WHILE LOOP - QUESTIONS

Video
00:30:44

CONDITIONAL STATEMENTS AND SIMPLE CALCULATOR PROGRAM

Video
00:16:38

LOOP CONTROL STATEMENTS

Video
00:05:41

Slot Booking

External Link

SET 3

WHILE LOOP AND PATTERN PRINTING

Video
01:00:37

PATTERNS

Video
01:00:30

STRINGS, INDEXING & SLICING

Video
00:32:32

BUILT IN FUNCTIONS IN DATA COLLECTION

Video
01:01:31

BUILT IN FUNCTIONS IN PYTHON LIST

Video
00:41:32

Slot Booking

External Link

SET 4

FUNCTIONS

Video
00:19:46

MODULES

Video
00:11:45

FILE HANDLING

Video
00:12:37

Slot Booking

External Link

SET 5

EXCEPTION HANDLING

Video
00:09:14

LISTS

Video
00:18:39

DICTIONARIES AND SETS

Video
00:15:10

MULTIPLE CONDITIONS

Video
00:05:19

NUMPY PANDAS

Video
01:16:10

Slot Booking

External Link

Module - 2: ML Algorithms and Techniques : SET 6

Introduction to AI & ML

Video
00:31:27

LOGISTIC REGRESSION

Video
00:07:53

Decision Trees

Video
00:20:22

Random Forest

Video
00:21:14

Slot Booking

External Link

SET 7

Introduction to AI & ML

Video
00:31:27

LOGISTIC REGRESSION

Video
00:07:53

Decision Trees

Video
00:20:22

Random Forest

Video
00:21:14

Slot Booking

External Link

SET 8

KNN Neighbors

Video
00:15:40

Unsupervised Learning

Video
00:16:13

Hierarchical Clustering

Video
00:20:59

PCA

Video
00:19:56

Slot Booking

External Link

SET 9

ANN

Video
00:13:38

Feed Forward Neural Networks

Video
00:11:51

Training in Perceptrons

Video
00:07:06

Cost function

Video
00:13:11

Slot Booking

External Link

SET 10

Mean Squared Error

Video
00:10:09

TensorFlow

Video
00:23:02

PyTorch

Video
00:16:06

Slot Booking

External Link

SET 11

Titanic Survival Prediction

Video
00:18:28

Titanic Survival Prediction Problem

Video
00:39:53

Titanic Survival Prediction Final Part

Video
00:11:48

Titanic Survival Prediction 2

Video
00:15:48

Slot Booking

External Link

Module - 3: Natural Language Processing : SET 12

NLP01

Video
00:35:01

NLP02

Video
00:34:43

NLP03

Video
00:30:53

Slot Booking

External Link

SET 13

NLP04 _Stemming

Video
00:42:30

NLP -05 lemmatization

Video
00:47:28

NLP06 POS Tagging Name entity recognitions

Video
00:34:46

Slot Booking

External Link

SET 14

NLP 07-SENTIMENT ANALYSIS-PROJECT

Video
00:34:21

NLP 08- BOW AND TFIDF.

Video
00:34:24

Slot Booking

External Link

SET 15

NLP-09 WORD EMBEDDING WORD2VEC

Video
00:41:43

NLP-10 GLOVE FASTTEXT

Video
00:37:17

Slot Booking

External Link

Module - 4: Computer Vision : SET 16

part 1

Video
00:32:24

SET 17

part 2

Video
00:29:23

Part 3

Video
00:13:10

Part 4

Video
01:02:34

SET 18

Introduction To CNN

Video
00:26:48

CNN PART 1

Video
00:29:01

CNN OPERATIONS PART 2

Video
00:53:52

CNN OPERATIONS PART 3

Video
01:08:45

Pooling

Video
00:43:16

CNN

Video
00:29:01

CVfinal

Video
00:38:04

SET 19

Transfer Learning and Resnet

Video
00:30:50

Notes

PDF

SET 20

Image Classification using ResNet-18 Pretrained

Video
00:32:16

SET 21

Object detection why classification is not en

Video
00:28:20

Notes

PDF

SET 22

YOLO Architecture Deep Dive

Video
00:30:25

Notes

PDF

SET 23

Object detetion using yolo and deepsort 2

Video
00:34:17

Notes

PDF

Module 5 : Generative AI : SET 24

Introduction to Generative AI and Its Applications

Video
00:28:07

Notes

PDF

SET 25

what are gans using cases and intitution

Video
00:29:44

Notes

PDF

SET 26

Implementing a simple GAN Using Pytorch Hands-on

Video
00:40:42

Notes

PDF

SET 27

Introduction to Prompt Engineering

Video
00:40:28

SET 28

Introduction to RNNS

Video
00:35:51

Notes

PDF

SET 29

Introduction of LSTM

Video
00:37:27

Notes

PDF

SET 30

Introduction of LLMs

Video
00:37:06

Notes

PDF

SET 31

LLAMA and open source LLMS

Video
00:32:40

Notes

PDF

SET 32

LSTM Handson using pytorch

Video
00:31:52

SET 33

GPT

Video
00:25:41

Notes

PDF

Module 6 : Data Analysis : SET 34

Types of Data Analytics

Video
00:27:05

SET 35

Types of Data

Video
00:17:56

SET 36

Data Collection Methods

Video
00:09:30

SET 37

Handling Missing Data

Video
00:38:19

SET 38

Dealing with Outliers and Anomalies

Video
00:38:44

SET 39

Data Normalizing and Scaling Techniques

Video
00:18:11

SET 40

Introduction : Exploratory Data Analysis (EDA)

Video
00:22:03

SET 41

Descriptive Statistics in Python

Video
00:24:38

SET 42

Data Visualization with Matplotlib & Seaborn

Video
00:26:28

SET 43

Correlation Analysis and Heatmaps

Video
00:42:06

SET 44

Creating Pair Plots and Box Plots

Video
00:22:14

SET 45

Introduction to Data Profiling and Data Quality

Video
00:23:44

SET 46

Data Visualization Power Bi & Tableow

Video
00:22:48

Notes

PDF

Course Instructor

tutor image

Academy Admin

28 Courses   •   143 Students

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Sreema V M

a year ago