Udemy – AI & Python Development Megaclass

Course content

Sections: 24

•

Activities: 0

•

Resources: 213

1 Introduction to Week 1 Python Programming Basics
2 Day 1 Introduction to Python and Development Setup
3 Day 2 Control Flow in Python
4 Day 3 Functions and Modules
5 Day 4 Data Structures Lists Tuples Dictionaries Sets
6 Day 5 Working with Strings
7 Day 6 File Handling
8 Day 7 Pythonic Code and Project Work

9 Introduction to Week 2 Data Science Essentials
10 Day 1 Introduction to NumPy for Numerical Computing
11 Day 2 Advanced NumPy Operations
12 Day 3 Introduction to Pandas for Data Manipulation
13 Day 4 Data Cleaning and Preparation with Pandas
14 Day 5 Data Aggregation and Grouping in Pandas
15 Day 6 Data Visualization with Matplotlib and Seaborn
16 Day 7 Exploratory Data Analysis EDA Project

17 Introduction to Week 3 Mathematics for Machine Learning
18 Day 1 Linear Algebra Fundamentals
19 Day 2 Advanced Linear Algebra Concepts
20 Day 3 Calculus for Machine Learning Derivatives
21 Day 4 Calculus for Machine Learning Integrals and Optimization
22 Day 5 Probability Theory and Distributions
23 Day 6 Statistics Fundamentals
24 Day 7 MathDriven Mini Project Linear Regression from Scratch

25 Introduction to Week 4 Probability and Statistics for Machine Learning
26 Day 1 Probability Theory and Random Variables
27 Day 2 Probability Distributions in Machine Learning
28 Day 3 Statistical Inference Estimation and Confidence Intervals
29 Day 4 Hypothesis Testing and PValues
30 Day 5 Types of Hypothesis Tests
31 Day 6 Correlation and Regression Analysis
32 Day 7 Statistical Analysis Project Analyzing RealWorld Data

33 Introduction to Week 5 Introduction to Machine Learning
34 Day 1 Machine Learning Basics and Terminology
35 Day 2 Introduction to Supervised Learning and Regression Models
36 Day 3 Advanced Regression Models Polynomial Regression and Regularization
37 Day 4 Introduction to Classification and Logistic Regression
38 Day 5 Model Evaluation and CrossValidation
39 Day 6 kNearest Neighbors kNN Algorithm
40 Day 7 Supervised Learning Mini Project

41 Introduction to Week 6 Feature Engineering and Model Evaluation
42 Day 1 Introduction to Feature Engineering
43 Day 2 Data Scaling and Normalization
44 Day 3 Encoding Categorical Variables
45 Day 4 Feature Selection Techniques
46 Day 5 Creating and Transforming Features
47 Day 6 Model Evaluation Techniques
48 Day 7 CrossValidation and Hyperparameter Tuning

49 Introduction to Week 7 Advanced Machine Learning Algorithms
50 Day 1 Introduction to Ensemble Learning
51 Day 2 Bagging and Random Forests
52 Day 3 Boosting and Gradient Boosting
53 Day 4 Introduction to XGBoost
54 Day 5 LightGBM and CatBoost
55 Day 6 Handling Imbalanced Data
56 Day 7 Ensemble Learning Project Comparing Models on a Real Dataset

57 Introduction to Week 8 Model Tuning and Optimization
58 Day 1 Introduction to Hyperparameter Tuning
59 Day 2 Grid Search and Random Search
60 Day 3 Advanced Hyperparameter Tuning with Bayesian Optimization
61 Day 4 Regularization Techniques for Model Optimization
62 Day 5 CrossValidation and Model Evaluation Techniques
63 Day 6 Automated Hyperparameter Tuning with GridSearchCV and RandomizedSearchCV
64 Day 7 Optimization Project Building and Tuning a Final Model

65 Introduction to Week 9 Neural Networks and Deep Learning Fundamentals
66 Day 1 Introduction to Deep Learning and Neural Networks
67 Day 2 Forward Propagation and Activation Functions
68 Day 3 Loss Functions and Backpropagation
69 Day 4 Gradient Descent and Optimization Techniques
70 Day 5 Building Neural Networks with TensorFlow and Keras
71 Day 6 Building Neural Networks with PyTorch
72 Day 7 Neural Network Project Image Classification on CIFAR10

73 Introduction to Week 10 Convolutional Neural Networks CNNs
74 Day 1 Introduction to Convolutional Neural Networks
75 Day 2 Convolutional Layers and Filters
76 Day 3 Pooling Layers and Dimensionality Reduction
77 Day 4 Building CNN Architectures with Keras and TensorFlow
78 Day 5 Building CNN Architectures with PyTorch
79 Day 6 Regularization and Data Augmentation for CNNs
80 Day 7 CNN Project Image Classification on Fashion MNIST or CIFAR10

81 Introduction to Week 11 Recurrent Neural Networks RNNs and Sequence Modeling
82 Day 1 Introduction to Sequence Modeling and RNNs
83 Day 2 Understanding RNN Architecture and Backpropagation Through Time BPTT
84 Day 3 Long ShortTerm Memory LSTM Networks
85 Day 4 Gated Recurrent Units GRUs
86 Day 5 Text Preprocessing and Word Embeddings for RNNs
87 Day 6 SequencetoSequence Models and Applications
88 Day 7 RNN Project Text Generation or Sentiment Analysis

89 Introduction to Week 12 Transformers and Attention Mechanisms
90 Day 1 Introduction to Attention Mechanisms
91 Day 2 Introduction to Transformers Architecture
92 Day 3 SelfAttention and MultiHead Attention in Transformers
93 Day 4 Positional Encoding and FeedForward Networks
94 Day 5 HandsOn with PreTrained Transformers BERT and GPT
95 Day 6 Advanced Transformers BERT Variants and GPT3
96 Day 7 Transformer Project Text Summarization or Translation

97 Introduction to Week 13 Transfer Learning and FineTuning
98 Day 1 Introduction to Transfer Learning
99 Day 2 Transfer Learning in Computer Vision
100 Day 3 FineTuning Techniques in Computer Vision
101 Day 4 Transfer Learning in NLP
102 Day 5 FineTuning Techniques in NLP
103 Day 6 Domain Adaptation and Transfer Learning Challenges
104 Day 7 Transfer Learning Project FineTuning for a Custom Task

105 Learn Python from Scratch Quick Tutorial
106 Day 1 Welcome Message Generator Print Statements Hello World
107 Day 2 Personalized Greeting Program Variables Data Types
108 Day 3 Simple Calculator User Input String Formatting
109 Day 4 Number Comparison Tool IfElse Statements
110 Day 5 Countdown Timer Loops For While
111 Day 6 Basic Math Quiz Game Functions
112 Day 7 Shopping List App Lists

113 Day 8 Contact Book Dictionaries
114 Day 9 Ingredient Checker Tuples Sets
115 Day 10 NoteTaking App File Handling
116 Day 11 Safe Calculator Exception Handling
117 Day 12 Temperature Converter Functions with Return Values
118 Day 13 Student Grade Manager List Comprehensions
119 Day 14 Random Password Generator Modules Libraries

120 Day 15 Recipe Viewer App Reading Files
121 Day 16 Daily Journal Logger Writing Files
122 Day 17 Student Report Generator CSV Files
123 Day 18 Mini ToDo App JSON Files
124 Day 19 Weather App using API APIs Basics
125 Day 20 Event Countdown Timer Dates Time
126 Day 21 Wikipedia Article Scraper Web Scraping

127 Day 22 Bank Account Simulator Classes Objects
128 Day 23 Library Management System Constructors Methods
129 Day 24 Employee Management System Inheritance
130 Day 25 Animal Sound Simulator Polymorphism
131 Day 26 Secure User Profile App Encapsulation
132 Day 27 Inventory Management System Static Class Methods
133 Day 28 Mini ATM Machine Final OOP Project

134 Day 29 Simple GUI App Tkinter Basics
135 Day 30 Click Counter App Buttons Events
136 Day 31 BMI Calculator Input Fields
137 Day 32 Drawing Pad App Canvas Widgets
138 Day 33 Simple Login System Message Boxes
139 Day 34 ToDo List GUI Advanced Widgets
140 Day 35 Expense Tracker App GUI Capstone

141 Day 36 Hello Flask App Flask Basics
142 Day 37 Personal Blog Website Routes Templates
143 Day 38 Contact Form App Forms User Input
144 Day 39 User Registration App Database Integration
145 Day 40 Mini Weather API REST APIs
146 Day 41 Deploy Flask App Deployment
147 Day 42 Portfolio Website Flask Capstone

148 Day 43 Matrix Calculator NumPy
149 Day 44 Data Cleaner Pandas
150 Day 45 Graph Plotter Matplotlib
151 Day 46 Sales Report Analyzer Data Analysis
152 Day 47 Temperature Plotter Plotting Trends
153 Day 48 Stock Price Tracker Data Scraping
154 Day 49 COVID19 Dashboard Capstone Project

155 Day 50 Weather Dashboard App
156 Day 51 Expense Tracker
157 Day 52 File Organizer Tool
158 Day 53 TicTacToe Game
159 Day 54 Mini Chatbot
160 Day 55 Music Playlist Organizer
161 Day 56 Personal Budget Planner
162 Day 57 ASCII Art Generator
163 Day 58 Pomodoro Timer
164 Day 59 Markdown to HTML Converter
165 Day 60 Personal Diary App

166 Day 61 Social Media Scraper
167 Day 62 Automated Backup Tool
168 Day 63 Movie Recommendation System
169 Day 64 PDF Merger Tool
170 Day 65 Portfolio Website Backend
171 Day 66 Flashcards Learning App
172 Day 67 Stock Market Dashboard
173 Day 68 Task Scheduler
174 Day 69 Currency Converter
175 Day 70 Data Visualizer App

176 Day 71 Spam Email Detector
177 Day 72 Text Sentiment Analyzer
178 Day 73 Handwriting Digit Recognition
179 Day 74 Voice Assistant
180 Day 75 Face Detection App
181 Day 76 Simple Recommendation System
182 Day 77 AI Chatbot with NLP
183 Day 78 Object Detection App
184 Day 79 Language Translator Tool
185 Day 80 Fake News Detector

186 Introduction to Machine Learning Algorithms and Implementation in Python
187 1 Supervised Learning Algorithms Linear Regression Implementation
188 2 Supervised Learning Algorithms Ridge and Lasso Regression Implementation
189 3 Supervised Learning Algorithms Polynomial Regression Implementation
190 4 Supervised Learning Algorithms Logistic Regression Implementation
191 5 Supervised Learning Algorithms KNearest Neighbors KNN Implementation
192 6 Supervised Learning Algorithms Support Vector Machines SVM Implementation
193 7 Supervised Learning Algorithms Decision Trees Implementation
194 8 Supervised Learning Algorithms Random Forests Implementation
195 9 Supervised Learning Algorithms Gradient Boosting Implementation
196 10 Supervised Learning Algorithms Naive Bayes Implementation
197 11 Unsupervised Learning Algorithms KMeans Clustering Implementation
198 12 Unsupervised Learning Algorithms Hierarchical Clustering Implementation
199 13 Unsupervised Learning Algorithms DBSCAN
200 14 Unsupervised Learning Algorithms Gaussian Mixture ModelsGMM
201 15 Unsupervised Learning Algorithms Principal Component Analysis PCA
202 16 Unsupervised Learning Algo tDistributed Stochastic Neighbor Embedding
203 17Unsupervised Learning Algorithms Autoencoders Implementation
204 18 SelfTraining Implementation
205 19 QLearning Implementation
206 20 Deep QNetworks DQN Implementation
207 21 Policy Gradient Methods Implementation
208 22 OneClass SVM Implementation
209 23 Isolation Forest Implementation
210 24 Convolutional Neural Networks CNNs Implementation
211 25 Recurrent Neural Networks RNNs Implementation
212 26 Long ShortTerm Memory LSTM Implementation
213 27 Transformers Implementation

Enrolment options

Udemy – AI & Python Development Megaclass – 300+ Hands-on Projects 2025-3

Course modified date: 7 Mar 2025

Enrolled students: There are no students enrolled in this course.

Guests cannot access this course. Please log in.

Udemy – AI & Python Development Megaclass – 300+ Hands-on Projects 2025-3

About the course

Course content

Instructors

Enrolment options

Udemy – AI & Python Development Megaclass – 300+ Hands-on Projects 2025-3

This course includes

Share this course