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AI/ML Course
Step into the booming field of Data Science by learning Deep Learning, machine learning algorithms, and statistical tools.
This course provides both foundational and advanced insights into the world of data. Whether you’re a beginner or have some experience, you’ll gain valuable knowledge and practical skills.
> Step 1: Practical Projects
2 live projects and 10+ demo projects to build your real-world experience.
> Step 2: Mock Interviews & Placement Support
Guidance on preparing for Machine Learning interviews and building a data-driven resume.
> Step 3: Trainer Expertise
Our trainers are Machine Learning with industry expertise, providing real-world insights and applications.
- 70 Hours Practical
- 50 Hours Practical
- 120 Total Hours
Step into the booming field of Machine Learning by learning Deep Learning, machine learning algorithms, and statistical tools.
- Online Training
- Offline Training
Course Syllabus
Downlaod Syllabus PDF
The Machine Learning course syllabus is designed to provide you with a strong foundation in both theoretical and practical aspects of Machine Learning.
Module 1: Machine Learning Basics
- Introduction to AI, ML, and DL
- Definitions and differences among Artificial Intelligence, Machine Learning, and Deep Learning.
- Scope, methods, and applications of each.
- Overview of the evolution of these technologies.
- Types of Machine Learning
- Supervised Learning: Definition, types, and examples.
- Unsupervised Learning: Definition, types, and examples.
- Reinforcement Learning: Basics and applications.
Module 2: Python Basics for Machine Learning
- NumPy, Pandas, Matplotlib, and Seaborn
- NumPy: Array creation, manipulation, and operations.
- Pandas: Data structures (Series and DataFrame), data ingestion, cleaning, and manipulation.
- Matplotlib & Seaborn: Data visualization techniques and advanced plotting capabilities.
- Scikit-learn: Overview of machine learning capabilities and implementation of algorithms.
Module 3: Python Libraries Tutorial for Machine Learning
- Introduction to Databases
- SQL Queries
- Data Extraction
- Joins
- Subqueries
- Aggregation Functions
Module 4: Data Collection and Processing
- Data Sourcing and Preparation
- Sources for data collection and methods (Kaggle API, web scraping).
- Handling missing values and standardization techniques.
- Label encoding and train-test split methods.
- Feature extraction for text data.
Module 5: Math Basics for Machine Learning
- Essential Mathematical Concepts
- Linear Algebra: Vectors, matrix basics, and operations.
- Statistics: Central tendencies, variability measures, hypothesis testing, and correlation.
- Probability: Basics, random variables, probability distributions (normal and Poisson).
Module 6: Training Machine Learning Models
- Understanding Machine Learning Models
- Types: Supervised and unsupervised models.
- Model selection, overfitting, bias-variance tradeoff, loss functions.
- Understanding model parameters and hyperparameters, and gradient descent.
Module 7: Models in Machine Learning
- Exploring Key ML Models
- Linear and logistic regression: Intuition, math, and implementation.
- Support Vector Machine (SVM): Theory, math, and building from scratch.
- Lasso regression, K-Nearest Neighbors (KNN), and Decision Trees: Concepts and Python implementations.
Module 8: Cross Validation, Hyperparameter Tuning and Model evaluation
- Optimizing Model Performance
- Techniques: K-fold cross-validation, Grid Search CV, and Randomized Search CV.
- Model evaluation metrics: Accuracy, precision, recall, and F1 score.
Module 9: Association Models in Machine Learning
- Association Rule Mining
- Apriori and Eclat algorithms: Theory, implementation, and applications.
Module 10: Machine Learning Projects in python
- Hands-on Projects
- Projects include Face Recognition, SONAR Rock vs Mine Prediction, Diabetes Prediction, House Price Prediction, Fake News Prediction, and Loan Status Prediction.
- Application of learned techniques to solve real-world problems.