Complete Machine Learning With Real-World Deployment

Course Curriculum

Chapter 1: Python Primer: A Beginner's Journey into Python's Fundamentals

Lecture 1: Python Essentials: Exploring Data Structures and String Operations

Lecture 2: Python Mastery: Harnessing the Power of Lambda, Recursion, and Functions

Lecture 3: Python for Data Analysis: Libraries, Exploratory Data Analysis, and Descriptive

Chapter 2: Machine Learning Foundations: A Beginner's Guide to the Basics of ML

Lecture 1: Machine Learning Primer: Exploring Logistic Regression – A Classical Algorithms

Lecture 2: Machine Learning Insights: Word Embedding Techniques – BoW, TF-IDF, Word2Vec

Lecture 3: Machine Learning Text Preprocessing: Cleaning and Preparing Amazon Reviews Data

Lecture 4: Machine Learning Fundamentals: Exploring Linear Regression – A Classical Algo

Lecture 5: Machine Learning Essentials: Understanding Decision Tree Classifier, Regression

Lecture 6: Machine Learning Insights: Geometric Intuition of Ensemble Models and Flask

Lecture 7: Machine Learning Data Analysis: Exploring Loan Approval Status with Predictive

Lecture 8: Machine Learning Unleashed: Unveiling K-means Clustering Techniques for Unsuperv

Chapter 3: Deep Learning Demystified: An Introduction to the Basics of Deep Learning

Lecture 1: Deep Learning Foundations: Exploring Neural Networks, MLP, and Backpropagation

Lecture 2: Deep Dive into Deep Learning:In-depth Understanding of RNN and LSTM with Example

Lecture 3: Deep Learning Insights: Unveiling the Intuition Behind Computer Vision and CNN

Lecture 4: Deep Learning Adventures: Mastering Convolutional Neural Networks with Pizza

Lecture 5: Deep Learning Practical Guide: Transfer Learning with VGG16 Model & Hands-on

Lecture 6: Deep Learning Web App: Building a Wild Animal Recognition System with CNN

Chapter 4: Time Series Insights: An Introduction to the Basics of Time Series Analysis

Lecture 1: Time Series Unveiled: Exploring Characteristics and Decomposition of Time Series

Lecture 2: Time Series Mastery: Best Practices in Probability, Statistics, and Forecasting

Lecture 3: Time Series Analysis in Practice: Exploring Medical Data for Practical Insights

Chapter 5: Flight Fare Prediction Project-1: Predicting and Analyzing Flight Ticket Prices

Lecture 1: Flight Fare Prediction Project-1: Predicting and Analyzing Flight Ticket Prices

Lecture 2: Feature Engineering and Classical ML Models: Enhancing Flight Fare Prediction

Lecture 3: Deploying the Flight Fare Prediction Model with Flask Framework: Making Predicti

Chapter 6: Mushroom Classification Project-2: Exploratory Data Analysis for Insightful

Lecture 1: Mushroom Classification Project-2: Exploratory Data Analysis for Insightful

Lecture 2: Building the Benchmark Model and Evaluation: Establishing a Baseline

Chapter 7: Nursery School Application Classification Project-3: Regression Analysis

Lecture 1: Nursery School Application Classification Project-3: Regression Analysis

Lecture 2: Logistic Regression, SVM, Decision Tree Models & Evaluation Metrics: Predictive

Chapter 8: Toxic Comments Classification Project-4 : Identifying and Analyzing Toxic

Lecture 1: Toxic Comments Classification Project-4 : Identifying and Analyzing Toxic

Lecture 2: NLP Visualization: Exploring Tokenized Sequences for Insightful Analysis

Lecture 3: Model Refinement: Optimizing Naive Bayes, SVM, and Logistic Regression with Feat

Chapter 9: UK Road Accident Timeseries Analysis: Exploratory Data Analysis for Forecasting

Lecture 1: UK Road Accident Timeseries Analysis: Exploratory Data Analysis for Forecasting

Lecture 2: Forecasting UK Accident Rates: SARIMA, FbProphet, and LSTM Models for Accurate