Intermediate
🎓 Certificate
PYTHON WITH AIML
Learn Python, AI & Machine Learning through practical training, industry-relevant algorithms, NLP, Flask deployment, and 5 real-time projects in 2 months.
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4.9
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2500+ Learners
60+ Hours
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About this Course
Start from zero-level Python programming and progress to Artificial Intelligence and Machine Learning. Learn Python fundamentals, NumPy, Pandas, data visualization, regression and classification algorithms, clustering, PCA, NLP, and Flask deployment through hands-on practical sessions. Build 5 major real-time projects and gain experience in developing and deploying machine learning applications.
Duration: 2 Months
Schedule: Monday to Friday, 8:00 PM – 9:00 PM
Training Mode: Online Live Classes with Recordings
What you'll learn
Write Python programs from zero level
Work with NumPy and Pandas for data analysis
Clean, process, and visualize datasets
Implement Regression and Classification algorithms
Apply K-Means, Hierarchical Clustering, and PCA
Build AI applications using NLP and text classification
Train and evaluate Machine Learning models
Develop and deploy ML web applications using Flask
Complete 5 major real-time AI/ML projects
Available Batches
Choose a batch that fits your schedule and budget
⭐ PREFERRED
🔔 UPCOMING
EVENING BATCH
₹15000.00
₹399.00
97% OFF
Mode
🌐 Online
Language
Telugu
Timings
8PM TO 9PM
Start Date
26 Oct 2026
Days
MON - FRI
9 slots left out of 20
11 filled
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Course Curriculum
- Introduction to Python
- Variables, Keywords and Identifiers
- Data Types and Type Casting
- Input and Output Operations
- Operators and Expressions
- Control Statements
- Functions
- Data Structures — Strings, Lists, Tuples, Sets and Dictionaries
- File Handling
- Exception Handling
- Modules, Packages and pip
- Coding Practise
- Introduction to NumPy and Installation
- Creating NumPy Arrays
- Array Attributes — Shape, Size, Dimensions and Data Type
- Indexing and Slicing
- Reshaping and Flattening Arrays
- Array Operations and Arithmetic
- Broadcasting and Vectorized Operations
- Mathematical and Statistical Functions
- Sorting, Searching and Filtering Arrays
- Joining and Splitting Arrays
- Random Number Generation
- Practical Applications of NumPy
- Introduction to Pandas and Installation
- Series and DataFrames
- Creating DataFrames from Lists, Dictionaries and NumPy Arrays
- Reading and Writing CSV and Excel Files
- Data Selection, Indexing and Filtering
- Adding, Updating and Removing Rows and Columns
- Sorting and Ranking Data
- Handling Missing Values
- Removing Duplicate Records
- Data Type Conversion
- Grouping and Aggregation
- Merging, Joining and Concatenating DataFrames
- Applying Functions and Data Transformation
- Date and Time Data Handling
- Exploratory Data Analysis (EDA)
- Introduction to Data Visualization
- Installing Matplotlib and Seaborn
- Figure, Axes and Plot Customization
- Line Plot
- Bar Chart and Horizontal Bar Chart
- Scatter Plot
- Histogram
- Pie Chart
- Box Plot
- Subplots and Multiple Charts
- Titles, Labels, Legends, Colors and Styles
- Saving Figures to Files
- Introduction to Seaborn and Dataset Loading
- Distribution Plots — Histplot and KDE Plot
- Count Plot and Bar Plot
- Scatter Plot and Line Plot
- Box Plot and Violin Plot
- Pair Plot
- Correlation Heatmap
- Categorical Data Visualization
- Introduction to Artificial Intelligence and Machine Learning
- Types of Machine Learning
- Supervised and Unsupervised Learning
- Machine Learning Workflow
- Dataset Preparation and Data Preprocessing
- Features and Target Variables
- Training and Testing Datasets
- Model Training and Prediction
- Model Evaluation Metrics
- Overfitting and Underfitting
- Introduction to Regression
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Decision Tree Regression
- Random Forest Regression
- K-Nearest Neighbors (KNN) Regression
- Support Vector Regression (SVR)
- Gradient Boosting Regression
- Model Training and Prediction
- Regression Model Evaluation — MAE, MSE, RMSE and R² Score
- Practical Implementation Using Scikit-learn
- Introduction to Classification
- Logistic Regression
- K-Nearest Neighbors (KNN) Classifier
- Decision Tree Classifier
- Random Forest Classifier
- Support Vector Machine (SVM)
- Naive Bayes Classifier
- Gradient Boosting Classifier
- AdaBoost Classifier
- XGBoost Classifier
- Classification Evaluation Metrics — Accuracy, Precision, Recall and F1-Score
- Confusion Matrix and Classification Report
- Practical Implementation Using Scikit-learn
- Introduction to Unsupervised Learning
- K-Means Clustering
- Elbow Method for Selecting Optimal Clusters
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- Cluster Visualization Using Matplotlib and Seaborn
- Practical Implementation Using Scikit-learn
- Introduction to Natural Language Processing (NLP)
- Text Preprocessing — Cleaning, Tokenization and Stop Words
- Text Vectorization — Bag of Words and TF-IDF
- Text Classification
- Sentiment Analysis
- Introduction to Generative AI and Large Language Models (LLMs)
- Introduction to Neural Networks and Deep Learning
- Introduction to Flask
- Creating Flask Applications
- HTML Forms and Templates
- Integrating ML Models with Flask
- Saving and Loading Models Using Pickle
- Accepting User Input and Generating Predictions
- Displaying Prediction Results on Web Pages
- Building and Running an ML Web Application
- House Price Prediction System
- Customer Churn Prediction System
- Customer Segmentation System
- Spam Message Detection and Sentiment Analysis
- Student Performance Prediction System
- Building ML Web Applications Using Flask
- Testing and Evaluating Project Results
Facilities Provided
🎥
Live Classes
📹
Recorded Classes
🎙️
Mock Interviews
💬
Spot Doubts
📄
Study Materials
🎓
Certificate
Quick Info
- LevelIntermediate
- Duration60+ hours
- Learners2500+
- Rating★ 4.9
- Certificate✅ Yes
- Internship❌ No
- Placement❌ No