Machine Learning Course in Irinjalakuda, Thrissur
JES Training Institute provides Machine Learning course and take a step closer to your career goal. This course is designed to meet your career aspirations by providing a handful of knowledge in Python, ML algorithms, statistics, supervised and unsupervised learning, among others. Machine Learning Course in Irinjalakuda from JES Training Institute is the right course that aligns with your career goals. With our support, multiple assignments, and several projects works, our best Machine Learning course helps you gain real-world exposure, learn ML and become a successful Machine Learning Engineer today.
Overview
This Machine Learning Program is curated and developed by leading faculty and industry leaders with Customized Specialisations. The course will nurture and transform you into a highly-skilled professional with an in-depth knowledge of various algorithms and techniques, such as regression, classification, supervised and unsupervised learning, Natural Language Processing, etc. Intellipaat’s best ML training also equi ps you to use Python programming language, which is a core to draw predictions from data.
Introduction :
1. Getting Started with Machine Learning
2. An Introduction to Machine Learning
3. What is Machine Learning ?
4. Introduction to Data in Machine Learning
5. Applications of Machine Learning
6. ML – Applications
7. Best Python librariesfor Machine Learning
8. Artificial Intelligence | An Introduction
9. Machine Learning and Artificial Intelligence
10. Difference between Machine learning and Artificial Intelligence
11. Agents in Artificial Intelligence
Data and It’s Processing:
1. Introduction to Data in Machine Learning
2. Understanding Data Processing
3. Python | Create Test DataSets using Sklearn
4. Python | Generate test datasetsfor Machine learning
5. Python | Data Pre-processing in Python
6. Data Cleansing
7. Feature Scaling – Part 1
8. Feature Scaling – Part 2
9. Python | Label Encoding of datasets
10. Python | One Hot Encoding of datasets
11. Handling Imbalanced Data with SMOTE and Near Miss Algorithm in Python
Supervised learning:
1. Getting started with Classification
2. Basic Concept of Classification
Types of RegressionTechniques
3. Classification vs Regression
4. ML | Types of Learning – Supervised Learning
5. Multiclass classification using scikit-learn
6. Gradient Descent:
- Gradient Descent algorithm and its variants
- Stochastic Gradient Descent (SGD)
- Mini-Batch Gradient Descent with Python
- Optimization techniquesfor Gradient Descent
- Introduction to Momentum-based Gradient Optimizer
7. Linear Regression:
- Introduction to Linear Regression
- Gradient Descent in Linear Regression
- Mathematical explanation for Linear Regression working
- Normal Equation in Linear Regression
- Linear Regression (Python Implementation)
- Univariate Linear Regression in Python
- Multiple Linear Regression using Python
- Locally weighted Linear Regression
- Python | Linear Regression using sklearn
- ML | Boston Housing Kaggle Challenge with Linear Regression
8. Python | Implementation of Polynomial Regression
9. Logistic Regression
- Understanding LogisticRegression
- Why LogisticRegression in Classification ?
- Logistic Regression using Python
- Cost function in LogisticRegression
10. Project 1: breast cancer prediction
11. Project 2: boston house prediction
12. Project 3: Autism Spectrum disorder prediction and classification and identify the best
algorithm in machine learning
