Machine Learning with Python
Embark on a journey into the real of Machine Learning coupled with Python, where theory meets practical application.
Overview
This course is a gateway to understanding the core concepts of ML, offering hands-on experience in utilizing Python’s robust libraries and tools. Through interactive sessions, grasp fundamental algorithms, explore data analysis, and learn to build predictive models. Gain insights into the world of AI-driven solutions, equipping yourself with the skills to tackle real-world challenges and make data-driven decisions.
Discover the power of Python as a tool for Machine Learning, unraveling its potential in transforming raw data into actionable insights. This course provides a comprehensive understanding of ML algorithms, enabling you to implement them efficiently using Python’s rich ecosystem. Dive deep into supervised and unsupervised learning techniques, pattern recognition, and data manipulation, all vital components in creating intelligent systems. Step into the real of predictive analysis and embrace the future of technology through the fusion of Machine Learning and Python programming.
Curriculum
- Getting Started with Machine Learning
- An Introduction to Machine Learning
- What is Machine Learning?
- Introduction to Data in Machine Learning
- ML – Applications
- Difference between Machine Learning and Artificial Intelligence
- Best Python Libraries for Machine Learning
- How Does Google Use Machine Learning?
- How Does NASA Use Machine Learning?
- 5 Mind-Blowing Ways Facebook Uses Machine Learning
- Targeted Advertising using Machine Learning
- How Machine Learning Is Used by Famous Companies?
- Understanding Data Processing
- Generate test datasets
- Create Test DataSets using Sklearn
- Data Preprocessing
- Data Cleansing
- Label Encoding of datasets
- One Hot Encoding of datasets
- Handling Imbalanced Data with SMOTE and Near Miss Algorithm in Python
- Types of Learning – Supervised Learning
- Getting started with Classification
- Types of Regression Techniques
- Classification vs Regression
- Introduction to Linear Regression
- Implementing Linear Regression
- Univariate Linear Regression
- Multiple Linear Regression
- Python | Linear Regression using sklearn
- Linear Regression Using Tensorflow
- Linear Regression using PyTorch
- Pyspark | Linear regression using Apache MLlib
- Boston Housing Kaggle Challenge with Linear Regression
- Polynomial Regression (From Scratch using Python)
- Polynomial Regression
- Polynomial Regression for Non-Linear Data
- Polynomial Regression using Turicreate
- Understanding Logistic Regression
- Implementing Logistic Regression
- Logistic Regression using Tensorflow
- Softmax Regression using TensorFlow
- Softmax Regression Using Keras
- Naive Bayes Classifiers
- Naive Bayes Scratch Implementation using Python
- Complement Naive Bayes (CNB) Algorithm
- Applying Multinomial Naive Bayes to NLP Problems
- Support Vector Machine Algorithm
- Support Vector Machines (SVMs) in Python
- SVM Hyperparameter Tuning using GridSearchCV
- Creating linear kernel SVM in Python
- Major Kernel Functions in Support Vector Machine (SVM)
- Using SVM to perform classification on a non-linear dataset
- Decision Tree
- Implementing Decision tree
- Decision Tree Regression using sklearn
- Random Forest Regression in Python
- Random Forest Classifier using Scikit-learn
- Hyperparameters of Random Forest Classifier
- Voting Classifier using Sklearn
- Bagging classifier
- K Nearest Neighbors with Python | ML
- Implementation of K-Nearest Neighbors from Scratch using Python
- K-nearest neighbor algorithm in Python
- Implementation of KNN classifier using Sklearn
- Imputation using the KNNimputer()
- - Implementation of KNN using OpenCV
- Types of Learning – Unsupervised Learning
- Clustering in Machine Learning
- Different Types of Clustering Algorithm
- K-means Clustering – Introduction
- Elbow Method for optimal value of k in KMeans
- K-means++ Algorithm
- Analysis of test data using K-Means Clustering in Python
- Mini Batch K-means clustering algorithm
- Mean-Shift Clustering
- DBSCAN – Density-based clustering
- Implementing DBSCAN algorithm using Sklearn
- Fuzzy Clustering
- Spectral Clustering
- OPTICS Clustering
- OPTICS Clustering Implementing using Sklearn
- Hierarchical clustering (Agglomerative and Divisive clustering)
- Implementing Agglomerative Clustering using Sklearn
- Gaussian Mixture Model
- Natural Language Processing (NLP)
Ready to delve into the world of Machine Learning with Python? Contact us today and take the first step toward mastering Machine Learning using Python.
Certificates We Provide
ALL CERTIFICATES ARE VALID BECAUSE THESE ARE CERTIFIED AND RECOGNIZED BY AICTE, IBM, ISO AND EURO
Stipend ranges from 15K to 40K based on performance and chance to work with MNC projects.
Course Completion Certificate
Internship And Project Completion Certificate
Letter of Recommendation.
Outstanding Performance Certificate.
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