Python & AI/ML/DL Training Institute
Python & AI ( Machine Learning / Deep Learning Program )
Develop advanced skills in Python programming, Machine Learning, Deep Learning, and Artificial Intelligence through hands-on projects and real-world datasets. This program prepares you to design intelligent systems, analyze data, and build AI-powered applications used across modern industries.
WHY CHOOSE THIS PROGRAM
Industry-aligned AI curriculum
Covers 20+ core AI and ML concepts, including Python, data analysis, machine learning algorithms, and deep learning tools used in modern industry.
Practical learning with real datasets and projects
Work on 10+ real datasets and 5+ industry-style projects to understand how AI models solve real-world problems
Instructor-led coding sessions and demonstrations
Includes 30+ guided coding sessions where instructors demonstrate model building, debugging, and best practices.
Hands-on ML and deep learning model development
Build 8+ machine learning and deep learning models using tools like Scikit-Learn, TensorFlow, and Keras.
CAREERS IN AI& MACHINE LEARNING
1.5 million
jobs in India (2026)
$279 billion
global market value
4 out of 5
companies use AI
Up to 22 lakhs
avg salary
Artificial Intelligence is one of the fastest-growing fields in technology, and companies increasingly rely on AI solutions to automate processes and analyze large datasets.
Career opportunities include:
• Python Developer
• Machine Learning Engineer
• Data Analyst / Data Scientist
• AI Engineer
• Computer Vision Engineer
• NLP Engineer
• Automation Engineer
These roles are available in IT companies, product startups, research organizations, and data-driven enterprises.
COMPREHENSHIVE CURRICULUM
- What is Python and why do you use it?
- Advantages of Python in near future
- Python syntax, variables, datatypes, operators with practice exercises
- Inbuild data structures, functions, conditional statements, and loops with practice exercises.
- File Handling and exception handling
- OOPS concepts with classes and objects.
- What is Machine Learning? (AI vs ML vs DL vs Genai)
- Types of ML: Supervised, Unsupervised, Semi-Supervised, Reinforcement Learning
- Basic workflow of ML model development
- Real-world applications (recommendation systems, fraud detection, CV, NLP)
- Numpy, Pandas (data manipulation)
- Matplotlib, Seaborn (visualization)
- Scikit-learn (ML library basics)
- Jupyter Notebook workflow
- Handling missing values, categorical encoding, normalization, standardization
- Outlier detection and treatment
- Feature selection techniques (Filter, Wrapper, Embedded methods)
- Regression
- Linear Regression
- Polynomial Regression
- Regularization (Ridge, Lasso)
- Classification
- Logistic Regression
- k-Nearest Neighbors
- Support Vector Machines
- Decision Trees & Random Forest
- Gradient Boosting (XGBoost, LightGBM, CatBoost)
- Model Evaluation Metrics: Accuracy, Precision, Recall, F1, ROC-AUC
- Clustering: K-Means, Hierarchical, DBSCAN
- Dimensionality Reduction: PCA, t-SNE, Autoencoders
- Association Rules: Apriori, Market Basket Analysis
- Train/Test Split, Cross-validation
- Bias-Variance Tradeoff
- Hyperparameter Tuning (Grid Search, Random Search, Bayesian Optimization)
- Overfitting & Underfitting
- What is Deep Learning?
- Artificial Neural Networks (ANN) basics
- Perceptron, Activation functions (ReLU, Sigmoid, Tanh, Softmax)
- Loss functions (MSE, Cross-Entropy)
- Gradient Descent & Backpropagation
- Frameworks: TensorFlow & PyTorch basics
- Types of YOLO models
- Overview on dataset preparation
- Differentiation of detection, classification, and segmentation models
- Real life implantations of deep learning models
- ANN (Dense Networks) – Tabular data
- CNN (Convolutional Neural Networks) – Computer Vision
- Convolution, Pooling, Padding, Dropout
- Architectures: LeNet, AlexNet, VGG, ResNet, EfficientNet, YOLO (basic intro)
- RNN (Recurrent Neural Networks) – Sequential Data
- RNN, LSTM, GRU
- Applications: Text, Speech, Time Series
- Transformers & Attention (Intro)
- Self-Attention mechanism
- BERT/GPT overview (not too deep for beginners)
- Fine-tuning on GPT 4o, Gemini with custom datasets.
- Libraries for NLP
- Normalizing textual data in NLP
- Text Representation and Embedding Techniques and Real-life examples in NLP
MASTER IN-DEMAND AI & ML TOOLS
CAREER SUPPORT
1:1 mentorship from industry experts
Get 1:1 career mentorship from our industry experts to prepare for jobs in AI and ML
Interview prep with experts
Participate in mock interviews and access our tips & hacks on the latest interview questions of top companies
Resume & profile review
Get your resume/cv and LinkedIn profile reviewed by our experts to highlight your AI & ML skills & projects
Access to RagatechSource Job Board
Apply directly to top opportunities from leading companies with our Job Board
DURATION
Course Length: 6 Months
Training: Up to 8 hours per day
Access: Recorded sessions available for revision
WHO CAN JOIN
- Students who want to start a career in Python programming
- Professionals looking to upskill or transition into software development or data-related roles
- Beginners who want to learn programming from scratch using Python
- Freelancers or entrepreneurs who want to build automation tools, scripts, or applications using Python
FREQUENTLY ASKED QUESTIONS (FAQs)
No prior programming experience is required. The course starts with Python fundamentals and gradually moves into machine learning and deep learning concepts.
Yes. The program is designed for beginners, students, and working professionals who want to build strong foundations in Python and AI through practical learning.
The course covers Python programming, data analysis, machine learning, deep learning, neural networks, computer vision, natural language processing, and real-world AI use cases.
Yes. Learners work on hands-on assignments, mini projects, and industry-aligned projects using real datasets to apply concepts practically.
The course includes tools and frameworks commonly used in the industry, such as Python libraries for data analysis, machine learning, and deep learning.
Yes. The course structure supports working professionals with guided sessions, practical assignments, and revision support.
Yes. Learners receive a course completion certificate after successfully finishing the Python & AI (ML, DL) training program.
After completing the course, learners can apply for roles such as Python Developer, Data Analyst, Machine Learning Engineer (Entry-Level), AI Engineer, and Data Science Associate.
Yes. The course builds a strong foundation that allows learners to progress into advanced AI, machine learning, or data science roles with further experience.
This course focuses on structured learning, hands-on projects, real-world datasets, and instructor guidance, going beyond isolated tutorials.