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 |
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Hands-On Projects |
✅ |
Job Assistance |
✅ |
Industry Experts |
✅ |
Affordable Pricing |
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Machine Learning course curriculum
There are Total 17 modules in this course:
- 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
- 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
- numpy
- scipy
- pandas
- matplotlib
- scikitlearn
- seaborn
- OpenCV
- keras
- TensorFlow
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
- 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
- Types of Data
- General Characteristics of dataset
- Data Pre-processing
- Feature scaling
- One Hot encoding
- outlier detection
- missing data handling & its implementation
- Types of Exploratory Data Analysis
- Evaluation Measures
- Measures of similarity and Dissimilarity
- K-Fold Cross Validation
- ROC curve
- Simple Linear Regression
- Gradient Descent & its implementation
- 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.
- Classification
- Decision Trees
- Regression Trees
- Supervised Learning with SVMs
- Supervised Learning with KNN
- Bias, Variance, and Ensemble 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
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
- Ridge Regression
- Lasso Regression
- Elastic Net Regression
- Implementation of these techniques.
- Naïve Bayes Classification
- Support Vector Machine (SVM)
- kernel SVM
- Implementation of these techniques
- Bagging ( eg., Random Forest)
- Boosting (eg., AdaBoost, Gradient Boosting)
- Stacking
- Model election & its implementation of these techniques.
- K-means & Hierarchical clustering
- Dendrogram
- implementation of these techniques