Best AI & ML and Data Science Course with PLACEMENT.
Python training program goes beyond the basics, providing hands-on experience and expert guidance. Join us for the best Python training in Bangalore and start a successful and rewarding career in this dynamic domain!
- 1st time in India !!! Experience hands-on learning through real-world industrial projects.
- Genuine Placement with upto 25LPA Package | Record of 5 to 10 Clients Hiring per week
- Dedicated HR & Accountability coach to track your daily and weekly Performance
Clients Hiring from Technologics Finishing School
Our trained students are being hired by more than 1500 premium clients from various Domains including e, Aerospace, Defence, medical Electronics, IOT and Home Appliances . The Offered Packages range from 3.5 LPA to 25 LPA, Based on the competency of our students demonstrated at their final round of client Assessment. Majority of our clients use online hiring system for their first round of assessment followed by F2F Technical & HR rounds. Success rate of TECHNOLOGICS Best Embedded training students solely depends on 85% of Coding Skill and rest on aptitude & MCQ.
EMBEDDED TRAINING OVERVIEW
Introduction to Python & IDE setup
Data types, operators, conditionals, loops
Functions, modules, and packages
Working with libraries: NumPy, Pandas, Matplotlib
File handling & data manipulation
Introduction to Databases & DBMS concepts
Data models and schemas
SQL Basics: SELECT, INSERT, UPDATE, DELETE
Advanced SQL: Joins, Subqueries, Views, Indexes, Transactions
Normalization & ER Diagrams
NoSQL Databases (MongoDB Basics)
Python connectivity with databases
Data collection and preprocessing
Data cleaning & transformation
Data visualization using Matplotlib, Seaborn, Power BI / Tableau (intro)
Exploratory Data Analysis (EDA)
Feature engineering and correlation analysis
Descriptive & inferential statistics
Probability & distributions
Linear algebra and calculus basics
Hypothesis testing and p-values
Correlation, covariance, and regression analysis
Linear & Logistic Regression
Decision Trees, Random Forests, SVM
K-Nearest Neighbors (KNN)
Naïve Bayes Classification
Introduction to AI & its applications
Search algorithms (BFS, DFS, A*)
Knowledge representation & reasoning
Expert systems basics
Introduction to Neural Networks & Deep Learning
Overview of Computer Vision & NLP applications
Course
Post Graduate Diploma
Complaince
ISO Standard
Skills
Advance Level
Placements
Yes
Certificate
Yes
Duration
90 Days
What Our student's Say About Us?
Persistence
Why join Technologics?
When aspiring to become a skilled python professional, selecting the best python training institute in Bangalore is of paramount importance. At TECHNLOGICS, we take pride in being the top choice for individuals seeking comprehensive training in python. Our institute has established itself as the best python training institute in Bangalore, offering a diverse range of courses designed to cater to the needs of both beginners and experienced professionals.
Our institute’s reputation as the best python training institute in Bangalore is further bolstered by our cutting-edge curriculum. We continuously update our course materials to keep pace with the rapidly evolving technology landscape, ensuring that our students are equipped with the most up-to-date skills and knowledge.
Technologies we work on
- 100% Placement Assistance Track record Till Date
- Fully Equipped Updated Software and Hardware facility
- Industrial experienced faculties
- New generation Hardware
- FREE Soft Skills Training
- 100% Hands-On Practical Oriented Training
- Career Guidance & Mentorship
- Individual Focus
- Real-Time Project Exposure
- Frequent accessibility to Industrial leadership
Best python Training INstitute in Bangalore
Training by Industrial Experts
A dedicated Industrial Trainer from the pool of 30+ R&D Engineers as accountability coach from TECHNOLOGICS Research Lab.
Accrediated
Certificate
Our Offline Python Course with placements is endorsed by Govt of India. Get Skill India Level-5 Certification .
Excellence in
Technology Skilling
Daily Live Master Class followed by Technical Discussion, Assignments, Coding practice powered by dedicated coding Leaders. Industrial level of weekly assessment with target score of 85% and above.
Get
Globally Recoginized
Enjoy excellent peer-to-peer
learning environment. Learn from physical
interaction with fellow students.
Genuine
Job Placements
Dedicated HR along with Industry Coach to Empower the Candidates facing Real-Time Industrial Interview, Unlimited Interview opportunities for every candidates with 85% and above Internal Score
Industry Specific Trade
Skills
Our Training content is Designed by Engineering team of TECHNOLOGICS Research Lab which is also vetted by Multiple Automotive Aerospace & Defense Clients.
Syllabus-In Detail
Description: Python is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python’s elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms.
Module 1: Python Programming
- Python setup and installation
- Data types: int, float, string, boolean
- Variables, Operators, Expressions
- Control structures: if, if-else, elif
- Loops: for, while, break, continue
- Functions, Lambda, Map/Reduce/Filter
- Data structures: Lists, Tuples, Sets, Dictionaries
- File handling, Exception handling
- Object-Oriented Programming (Classes, Inheritance)
- Modules and Libraries
- List/Dictionary Comprehensions
- Decorators, Iterators, Generators
Module 2: Statistics & Probability
- Descriptive Statistics: mean, median, mode, std, range
- Inferential Statistics: population vs sample
- Probability rules: independent/dependent events
- Distributions: Normal, Binomial, Poisson
- Hypothesis Testing (t-test, ANOVA, Chi-square)
- Confidence Intervals, p-values, z-scores
- A/B Testing & Experimental Design
- Central Limit Theorem
- Correlation & Covariance
Module 3: Linear Algebra & Optimization
- Scalars, Vectors, Matrices
- Matrix operations (addition, multiplication, transpose)
- Determinant, Inverse, Eigenvalues & Eigenvectors
- Gradient Descent and Cost Functions
- Learning rate, Convex vs Non-convex optimization
Module 4: Data Handling with NumPy & Pandas
- NumPy arrays, array indexing, broadcasting
- Matrix operations with NumPy
- Pandas Series & DataFrame creation
- Reading/writing CSV, Excel, JSON
- Data inspection: .head(), .info(), .describe()
- Data cleaning: missing values, duplicates
- GroupBy, Merge, Join, Concatenate
Module 5: Data Visualization (Python)
- Matplotlib: line, bar, pie, histogram, scatter
- Plot customization: titles, labels, legends
- Subplots and multi-axes plots
- Seaborn: heatmaps, pairplots, violin plots, box plots
- Advanced plots: FacetGrid, jointplot
Module 6: SQL
- SQL: SELECT, WHERE, GROUP BY, HAVING
- Joins: INNER, OUTER, LEFT, RIGHT
- Subqueries and Aggregate Functions
- DDL, DML, DCL statements
Module 7: MongoDB
- MongoDB: collections, documents, CRUD operations
- Indexing and querying in NoSQL
Module 8: Git & Version Control
- Version control basics and need
- Git: init, add, commit, status, log
- GitHub setup and repository management
- Branching, Merging, Resolving conflicts
- Reverting and resetting commits
- Collaborating in teams using GitHub/Bitbucket
Module 9: Exploratory Data Analysis (EDA)
- Data distributions and statistical summaries
- Outlier analysis and treatment
- Missing value imputation techniques
- Correlation matrix and heatmaps
- Feature identification and data patterns
Module 10: Machine Learning Basics
- Supervised vs Unsupervised Learning
- ML pipeline and data preprocessing
- Classification, Regression, Clustering
- Model training, testing, validation
- Evaluation metrics: accuracy, precision, recall, F1
Module 11: Machine Learning Algorithms
- Linear Regression & Logistic Regression
- K-Means Clustering
Module 12: Ensemble Learning
- Bagging, Boosting, Voting Classifier
- Random Forest
- Gradient Boosting, XGBoost, LightGBM
- Hyperparameter tuning (GridSearch, RandomSearch)
Module 13: Feature Engineering
- Encoding: One-hot, Label Encoding
- Normalization and Standardization
- Feature selection methods
- Creating new features
- Outlier treatment
- Pipelines and transformers in Scikit-learn
Module 14: Time Series Forecasting
- Time series components: trend, seasonality
- Stationarity, autocorrelation, ACF, PACF
- ARIMA, SARIMA models
- Forecast accuracy metrics
- Time series project using Python
Module 15: Deep Learning & Neural Network
- Neural network structure: layers, weights, activation
- Forward & Backward propagation
- Activation functions: ReLU, Sigmoid, Tanh, Softmax
- TensorFlow & Keras for neural networks
- Loss functions and optimizers (SGD, Adam)
Module 16: Natural Language Processing (NLP)
- Text preprocessing: Tokenization, Lemmatization
- TF-IDF, Bag of Words, Word2Vec, GloVe
- Text classification using Naive Bayes, SVM
- Sentiment analysis
- Sequence models: RNN, LSTM
- Transformers, BERT, GPT overview
Module 17: Computer Vision
- Image basics and preprocessing
- Convolutional Neural Networks (CNN)
- Object Detection: YOLO, RCNN, SSD
- Transfer Learning using pre-trained models
- OpenCV basics
Module 18: Model Deployment
- Flask basics: API, routing, endpoints
- Deploying models as REST APIs
- Streamlit dashboard creation
- Docker basics for containerization
- Introduction to AWS, Azure, GCP
Module 19: Power BI for Data Analytics
- Connecting data sources (Excel, SQL, Web)
- Creating dashboards, slicers, filters
- Using Power Query for data transformation
- DAX functions: SUMX, CALCULATE, IF
- Reports and publishing to Power BI Service
Module 20: Tableau for Business Intelligence
- Tableau workspace and user interface
- Connecting to data sources
- Creating visualizations: bar, line, scatter, maps
- Parameters, Filters, Calculated Fields
- Dashboard creation and sharing
Module 21: Reinforcement Learning
- Markov Decision Processes (MDP)
- Q-Learning, SARSA
- Deep Q Networks (DQN)
- OpenAI Gym environments
Module 22: Generative AI & Prompt Engineering
- GANs: architecture and training
- Introduction to Transformers, GPT
- Prompt design for LLMs (ChatGPT, BERT)
- Applications: image generation, text summarization
- Building chatbot with HuggingFace APIs
Capstone Projects
- Minimum 5 Capstone Projects (ML, NLP, CV, BI, DL)
- 1 Client/Live Project (applicable only if there is any ongoing projects)
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If you’re searching for the Best Python Training Institute in Bangalore, look no further than TECHNOLOGICS. Join us today to kickstart your journey towards a successful and rewarding career in python. Register now
