Introduction to the Course
Welcome to the Machine Learning with Python course! This course will introduce you to the fundamentals of machine learning, helping you understand how to implement algorithms using Python.
Who is this course for?
- Freshers with any Graduation degree
- Professionals who want to switch from NON-IT to IT
- Professionals who want to boost there career
Requirements
Before starting this course, it is recommended that you have basic knowledge of:
- Basic understanding of Python Programming Language
- Familiarity with basic Python knowledge is strongly recommended
- Basic Programming concepts like Variables, Data Types and Basic Arithmetic
- Mathematics (especially algebra and statistics).
- Basic understanding of programming concepts and algorithms.
What You'll Learn
Introduction▶
- Introduction.
- How to Succeed in This Course.
- Mathematical background of machine learning.
- Data split, Test train validate datasets.
Classification of ML▶
- Introduction to Classification of ML.
- Understanding MNIST.
- SGD.
- Performance Measure and Stratified k-Fold.
- Confusion Matrix.
- Precision.
- Recall.
- f1.
- Precision Recall Tradeoff.
- Altering the Precision Recall Tradeoff.
- ROC.
Support Vector Machine▶
- Support Vector Machine (SVM) Concepts.
- Linear SVM Classification.
- Polynomial Kernel.
- Radial Basis Function.
- Support Vector Regression.
Tree▶
- Introduction to Decision Tree.
- Training and Visualizing a Decision Tree.
- Visualizing Boundary.
- Tree Regression, Regularization and Over Fitting.
- End to End Modeling.
Ensemble Machine Learning▶
- Ensemble Learning Methods Introduction.
- Bagging.
- Random Forests and Extra-Trees.
- AdaBoost.
- Gradient Boosting Machine.
- XGBoost Installation.
- XGBoost.
k-Nearest Neighbours▶
- kNN Introduction.
Unsupervised learning Dimensionality Reduction ▶
- Dimensionality Reduction Concept.
- PCA Introduction.
- Project Wine.
- Kernel PCA.
- Kernel PCA Demo.
- LDA vs PCA.
- Clustering.
- k_Means Clustering.
Deep Learning▶
- Estimating Simple Function with Neural Networks.
- Neural Network Architecture.
- Motivational Example - Project MNIST.
- Binary Classification Problem.
- Natural Language Processing - Binary Classification.
Foundation to Deep Learning▶
- Introduction to Neural Networks.
- Differences between Classical Programming and Machine Learning.
- Learning Representations.
- What is Deep Learning.
- Learning Neural Networks.
- Why Now?.
- Building Block Introduction.
- Tensors.
- Tensor Operations.
- Gradient Based Optimization.
- Getting Started with Neural Network and Deep Learning Libraries.
- Categories of Machine Learning.
- Over and Under Fitting.
- Machine Learning Workflow.
Trainer Expertise
This program is monitored by a team of professionals. We have crafted this program using the learnings of 23+ years of experience handling corporate training and job oriented training. Our students are working in almost all top MNCs across India.
Job Opportunities
100% placement record — each student successfully transitioned into a desired Machine Learning career role.
Course Duration
16 Weeks
Fees
Training + Job Assistance: ₹35,000
- Admission: ₹10,000
- After 1 month: ₹25,000