8977525889,8977625889

Machine Learning course Hyderabad online

18 of Modules Machine Learning course in Hyderabad online

Machine Learning with Python helps you create smart systems. Using Python libraries like NumPy, Pandas, Scikit-learn, and Matplotlib, this course teaches you how to work with data, build models, and visualize results, making it perfect for anyone interested in AI and data analysis.

Why Choose TronixTechs for Machine Learning?

Looking to master Machine Learning and take your career to the next level? At TronixTechs, we offer a comprehensive and industry-recognized Machine Learning Course designed for students, job seekers, and professionals. Our hands-on approach ensures you gain practical knowledge and real-world expertise.

Course Highlights & Benefits

  • Industry-Recognized Certification – Stand out with an accredited ML certificate.
  • Hands-On Projects – Work on real-time AI & ML projects.
  • Expert Instructors – Learn from industry leaders and ML specialists.
  • Career Support – Resume-building, mock interviews, and job assistance.
  • Flexible Learning – Online classes with live and recorded sessions.

What You Will Learn

  • 📌 Supervised & Unsupervised Learning
  • 📌 Neural Networks & Deep Learning
  • 📌 Data Preprocessing & Feature Engineering
  • 📌 Python for ML & AI
  • 📌 Model Deployment & Optimization

How Our ML Course in Tronix

Feature TronixTechs
Live Training
Hands-On Projects
Job Assistance
Industry Experts
Affordable Pricing

Machine Learning course curriculum

There are Total 17 modules in this course:

+ Module-1: Introduction to Machine Learning
  • Introduction to Machine learning (ML)
  • Types of Machine Learning(Branches of Machine Learning)
  • Applications of Machine Learning
  • A Day in the life of a Machine Learning Engineer
  • skills for Machine Learning
  • Tools for Machine Learning
  • Machine Learning Model Lifecycle
+ Module-2: Python programming Language Basics
  • Introduction
  • Download and Install python
  • Running python through the console
  • Using Python through an Integrated Development Environment
  • Basic Examples
  • Control Structures 32
  • Python built-in Data Structures
  • Functions
  • csv files
  • Making Python Interactive
  • Installing and Using Python Packages
  • Spyder
  • Jupyter Notebook
  • Google Colab
+ Module-3: python Libraries
  • numpy
  • scipy
  • pandas
  • matplotlib
  • scikitlearn
  • seaborn
  • OpenCV
  • keras
  • TensorFlow
+ Module-4: Mathematics for ML:
    Linear Algebra:
  • Vectors and matrices
  • Linear Equations
  • Eigenvalues and Eigenvectors
  • Matrix Transpose and Inverse
  • Matrix Multiplication and Factorization
  • Linear Transformations
  • Linear Regression
  • Calculus:
  • Differentiation (Limit, Continuity and Partial derivatives)
  • Multivariable Calculus (optional, but beneficial)
  • Differential and Integral Calculus
  • Integration
  • Gradient Descent
  • Maxima and Minima of a Function
  • Step, Logit, Sigmoid, and ReLU Function
+ Module-5: Probability and Statistics for ML:
  • Probability Distributions (Normal, Binomial, Poisson, etc.)
  • Descriptive Statistics (Mean, Median, Standard Deviation)
  • Hypothesis Testing (Null Hypothesis, p-value)
  • Statistical Learning Theory (Bias-Variance Tradeoff)
  • Bayesian Statistics (optional, but useful for certain applications)
  • Regression Analysis
  • Conditional Probability
+ Module-6: Data & Dataset
  • Types of Data
  • General Characteristics of dataset
  • Data Pre-processing
  • Feature scaling
  • One Hot encoding
  • outlier detection
  • missing data handling & its implementation
+ Module-7: Exploratory Data Analysis
  • Types of Exploratory Data Analysis
  • Evaluation Measures
  • Measures of similarity and Dissimilarity
  • K-Fold Cross Validation
+ Module-8: AUC ROC Curve in Machine Learning
  • ROC curve
  • Simple Linear Regression
  • Gradient Descent & its implementation
+ Module-9: Linear and Logistic Regression
  • Introduction to Regression
  • Understanding Linear Regression
  • Why Linear Regression is Important
  • what is the best Fit Line
  • Types of Linear Regression
  • Introduction to Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial and Non-Linear Regression
  • Introduction to Logistic Regression
  • Training a Logistic Regression Model
  • Polynomial Linear Regression & its implementation.
+ Module-10 : Building Supervised Learning Models:
  • Classification
  • Decision Trees
  • Regression Trees
  • Supervised Learning with SVMs
  • Supervised Learning with KNN
  • Bias, Variance, and Ensemble Models
+ Module-11: Building Unsupervised Learning Models
  • Clustering Strategies and Real-World Applications
  • K-means and More on K-means
  • DBSCAN and HDBSCAN Clustering
  • Clustering, Dimension Reduction & Feature Engineering
  • Dimension Reduction Algorithms
+ Module-12: Reinforcement Learning
    Model-Free Methods
  • Q-Learning
  • Deep Q-Network (DQN)
  • SARSA (State-Action-Reward-State-Action)
  • Policy Gradient Methods (e.g., REINFORCE)
  • Model-Based Methods
  • Deep Deterministic Policy Gradient (DDPG)
  • Proximal Policy Optimization (PPO)
  • Trust Region Policy Optimization (TRPO)
  • Value-Based Methods
  • Monte Carlo Methods
  • Temporal Difference (TD) Learning
+ Module-13: Regularization in Machine Learning:
  • Ridge Regression
  • Lasso Regression
  • Elastic Net Regression
  • Implementation of these techniques.
+ Module-14: Naive Bayes vs. SVM for Text Classification
  • Naïve Bayes Classification
  • Support Vector Machine (SVM)
  • kernel SVM
  • Implementation of these techniques
+ Module-15: Ensemble Learning:
  • Bagging ( eg., Random Forest)
  • Boosting (eg., AdaBoost, Gradient Boosting)
  • Stacking
  • Model election & its implementation of these techniques.
+ Module-16: Clustering:
  • K-means & Hierarchical clustering
  • Dendrogram
  • implementation of these techniques
Module-17: OpenCV, Face Recognition
Final project and Exam

Register for Course!

Learn from the Real-Time Experts