Introduction to Course

0m 31s

Delegate Pack

Introduction to Modules

1m 43s

Pre course Reading

SANDBOX: Python with Machine Learning

What is Machine Learning

7m 1s

Module Assessment 1 (Topic 1)

Classification of Machine Learning

5m 11s

Module Assessment 1 (Topic 2)

Reinforcement Learning

6m 36s

Module Assessment 1 (Topic 3)

Datasets for ML

6m 48s

Module Assessment 1 (Topic 4)

Datasets and Application of Machine Learning

7m 36s

Module Assessment 1 (Topic 5)

Application and Python libraries for ML

6m 51s

Module Assessment 1 (Topic 6)

Case Study - Implementing Machine Learning for Online Fraud Detection

Case Study - Product Recommendation Systems

Introduction to Regression

6m 16s

Regression analysis and Types of Regression

5m 3s

Module Assessment 2 (Topic 1)

Linear Regression

8m 59s

Linear Regression and its types, Polynomial Regression

5m 57s

Non-linear Regression, Model Evaluation process, Cross-validation in ML

6m 39s

Module Assessment 2 (Topic 2)

Types of Predictive Model, AUC-ROC

6m 27s

Module Assessment 2 (Topic 3)

ROC & AUC curve, Mean Squared Error, K-fold cross validation

8m 27s

Module Assessment 2 (Topic 4)

Case Study - Enhancing Credit Risk Assessment Using Logistic Regression

Case Study - Predicting Housing Prices Using Linear Regression

Matching Regression Types with Definitions: Interactive Learning Activity

0m 0s

Understanding Regression: Key Concepts and Terminologies Quiz

0m 0s

Key Formulas in Regression: Fill in the Blanks Exercise

0m 0s

Introduction to Classificaton & Classifier

7m 19s

K-Nearest Neighbour (KNN) & Decision Tree

7m 18s

Why to use Decision Tree and its Terminologies

9m 33s

Decision tree steps, Advantages and Disadvantages of Decision Tree, Logistic Regression

6m 20s

Module Assessment 3 (Topic 1)

Logistic Functions, Equation and Types of Logistic Regression

6m 32s

Support Vector Machine, Naive Bayes, Bayes Theorem

6m 17s

Advantages and Disadvantages of NB classifier, Random Forest Classification

5m 9s

Hands-On Logistic Regression

21m 40s

Module Assessment 3 (Topic 2)

Case Study - Implementing K-Means Clustering for Customer Segmentation

Case Study - Data Pre-Processing Techniques for Prompt Engineering

Choosing the Right Classifier: Classification Scenarios Quiz

0m 0s

True or False: Testing Your Knowledge on Classification Algorithms

0m 0s

Exploring Classification Algorithms: An Accordion Guide to Classifiers

0m 0s

Introduction to Unsupervised Learning, Types of Unsupervised Algorithm​

8m 23s

Advantages and Disadvantages of UL, UL Algorithms, K-means Clustering, Elbow Method

8m 38s

Hierarchical Clustering, Density Based Clustering

6m 15s

Apriori Algorithm and its Advantages & Disadvantages

4m 51s

Hands-On Clustering

10m 48s

Module Assessment 4

Uncover the Terms: Unsupervised Learning Word Search

0m 0s

Visual Match-Up: Unsupervised Learning Algorithms Memory Game

0m 0s

Visualising Unsupervised Learning: Before and After Algorithm Application

0m 0s

Dimensionality Reduction and its Needs

7m 54s

Types of Dimensionality Reduction, PCA, Variance

7m 38s

Covariance, Correlation, Application of PCA, P-value

7m 24s

Hypothesis Testing, Hypothesis in Statistics, Critical value, Statistical significance

8m 31s

Errors in P-value, LDA

6m 19s

Difference between PCA & LDA, Overfitting, Underfitting

5m 36s

Hands on PCA

7m 7s

Module Assessment 5

Decoding Dimensionality: A Crossword on Reduction Techniques

0m 0s

Step-by-Step Evolution: PCA Algorithm Timeline

0m 0s

Visualising Dimensionality Reduction: Before and After

0m 0s

Introduction to Deep Learning and its Importance

7m 44s

Neural Networks

5m 35s

SOMs, DBNs, RBMs, ANNs

7m 4s

Module Assessment 6

Case Study - Application of CNNs in Medical Imaging for Disease Diagnosis

Case Study - Development of Autonomous Vehicles at XYZ Automotive

Deep Dive into Neural Networks: Section-wise Quiz

0m 0s

Identifying Neural Networks: A 'Guess the Answer' Challenge

0m 0s

Deep Learning Essentials: Concepts, Algorithms, and Applications

0m 0s

Post Quiz 1

Post Quiz 2

Let's prepare for the Interview