Machine Learning Course Overview

Master Machine Learning concepts, tools, and real-world applications in one comprehensive and hands-on training program!

Rohil NextGen offers a complete Machine Learning training covering Python, NumPy, Pandas, Scikit-learn, Matplotlib, TensorFlow, Keras, and model deployment using Flask and Streamlit.

TOP RATED 4.9 4.9 Ratings
4 Months
Course Duration
160
Total Hours
10
Live Projects
2025
Latest Curriculum

Why Choose Machine Learning?

Annual Income

Avg. Salary in India

₹ 15 L

Career Growth

In AI, Data Science, Robotics

Explosive

Tech Stack

Python, Scikit-learn, TensorFlow, Keras

Cutting-edge

Become a Machine Learning Engineer

❝ Learn Python, Algorithms, Deep Learning & Model Deployment ❞

Course Overview

This Machine Learning course teaches you how to analyze data, build predictive models, and create intelligent systems using Python, Scikit-learn, Pandas, NumPy, Matplotlib, TensorFlow, and Keras. You'll also learn how to deploy ML models using Flask and Streamlit.

Key Learning Outcomes

  • Code ML algorithms using Python.
  • Visualize data using Matplotlib and Seaborn.
  • Train & evaluate models with Scikit-learn.
  • Build neural networks with TensorFlow & Keras.
  • Deploy models using Flask & Streamlit.

Career Prospects with This Course

  • Machine Learning Engineer
  • Data Scientist
  • Data Analyst
  • AI Engineer
  • Deep Learning Specialist

Course Syllabus

Week 1-2: Python & Math Foundations

Theory

  • Python Basics: Variables, Loops, Functions, Classes
  • NumPy, Pandas
  • Math for ML: Linear Algebra: vectors, matrices, dot product
  • Calculus: derivatives, gradients basics
  • Probability & Statistics: mean, variance, probability basics

Practical

  • Write math functions from scratch (mean, std, etc.)
  • Work with small datasets (CSV) using Pandas
  • Mini project: Data profiling report

Week 3-4: Data Preprocessing & EDA

Theory

  • Data cleaning and preprocessing techniques
  • Exploratory Data Analysis (EDA)
  • Feature engineering and selection
  • Handling missing values and outliers

Practical

  • Clean and preprocess real-world datasets
  • Perform comprehensive EDA
  • Feature engineering project

Week 5-6: Supervised Learning – Regression

Theory

  • Linear Regression and its variants
  • Polynomial Regression
  • Regularization techniques (Ridge, Lasso)
  • Evaluation metrics for regression

Practical

  • Implement various regression algorithms
  • Build housing price prediction model
  • Compare model performance

Week 7-8: Supervised Learning – Classification

Theory

  • Logistic Regression
  • K-Nearest Neighbors (KNN)
  • Support Vector Machines (SVM)
  • Decision Trees and Random Forests

Practical

  • Build classification models
  • Implement customer churn prediction
  • Create spam detection system

Week 9-10: Model Evaluation & Optimization

Theory

  • Cross-validation techniques
  • Hyperparameter tuning
  • Model evaluation metrics
  • Bias-Variance tradeoff

Practical

  • Implement cross-validation
  • Perform hyperparameter optimization
  • Model comparison project

Week 11-12: Unsupervised Learning – Clustering

Theory

  • K-Means Clustering
  • Hierarchical Clustering
  • DBSCAN and other clustering algorithms
  • Dimensionality Reduction (PCA)

Practical

  • Implement customer segmentation
  • Apply dimensionality reduction
  • Clustering analysis project

Week 13-14: Intro to Neural Networks & Deep Learning

Theory

  • Neural Network fundamentals
  • Activation functions
  • Backpropagation algorithm
  • Gradient descent optimization

Practical

  • Build basic neural networks from scratch
  • Implement backpropagation
  • Simple classification with neural networks

Week 15-16: Deep Learning with TensorFlow/Keras

Theory

  • TensorFlow and Keras fundamentals
  • Building and training deep learning models
  • Model saving and loading
  • Callbacks and early stopping

Practical

  • Build deep learning models with TensorFlow
  • Image classification project
  • Model optimization techniques

Week 17-18: Advanced Neural Networks & CNNs

Theory

  • Convolutional Neural Networks (CNNs)
  • CNN architectures (LeNet, AlexNet, VGG)
  • Transfer learning
  • Data augmentation techniques

Practical

  • Build CNN for image recognition
  • Implement transfer learning
  • Advanced image classification project

Week 19-20: Final AI Project & Model Deployment

Theory

  • Model deployment strategies
  • Flask and Streamlit for web deployment
  • Cloud deployment options
  • Model monitoring and maintenance

Practical

  • Complete capstone ML project
  • Deploy model using Flask/Streamlit
  • Create ML portfolio and documentation

Frequently Asked Questions

What is Machine Learning?

Machine Learning is a subset of artificial intelligence that enables computers to learn and make decisions from data without being explicitly programmed. It focuses on developing algorithms that can identify patterns and make predictions.

Do I need strong math background for Machine Learning?

While a math background is helpful, our course covers all necessary mathematical concepts from basics. We teach linear algebra, calculus, and statistics as part of the curriculum, making it accessible to beginners.

Will I receive a certificate after completing the Machine Learning course?

Yes, upon successful completion of the course and final project, you will receive a certificate from Rohil NextGen that validates your Machine Learning skills.

What kind of projects will I work on during the course?

You'll work on real-world projects including data analysis, predictive modeling, image classification, customer segmentation, and a capstone project that integrates all the skills you've learned.

What tools and technologies will I learn?

You'll learn Python, NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn, TensorFlow, Keras, Flask, Streamlit, and various machine learning algorithms and techniques.

What career support do you provide after course completion?

We provide resume building assistance, interview preparation, portfolio development guidance, and connect you with our placement partners for job opportunities in the AI and data science industry.