Machine Learning

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Machine Learning

Machine Learning empowers learners to create predictive models and intelligent applications. The course includes supervised and unsupervised learning, model evaluation, and deployment techniques, giving learners practical exposure to solving AI-driven challenges.

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Course Curriculum

Introduction to Machine Learning

What is Machine Learning?
Types of ML: Supervised, Unsupervised, Reinforcement Learning
Applications and Use Cases Across Industries

Python for Machine Learning

Python Basics for ML (variables, loops, functions)
Libraries: NumPy, Pandas, Matplotlib, Seaborn
Data Manipulation and Visualization

Data Preprocessing \& Feature Engineering

Handling Missing Data
Encoding Categorical Variables
Feature Scaling (Normalization & Standardization)
Feature Selection Technique

Exploratory Data Analysis (EDA)

Understanding Data Distributions
Correlation and Outlier Detection
Data Visualization Techniques

Supervised Learning Algorithms

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

Unsupervised Learning Algorithms

K-Means Clustering
Hierarchical Clustering
Principal Component Analysis (PCA)

Model Evaluation and Validation

Train-Test Split and Cross-Validation
Evaluation Metrics: Accuracy, Precision, Recall, F1-Score, ROC-AUC
Bias-Variance Tradeoff

Model Tuning \& Optimization

Hyperparameter Tuning: Grid Search and Random Search
Overfitting vs Underfitting
Regularization Techniques (L1, L2)

Introduction to Deep Learning

Neural Networks Basics
Activation Functions and Layers
Forward and Backpropagation
Introduction to TensorFlow/Keras

Natural Language Processing (NLP)

Text Preprocessing (Tokenization, Stop Words, Lemmatization)
Bag of Words and TF-IDF
Sentiment Analysis and Text Classification

Time Series Analysis

Time Series Concepts
Forecasting Models (ARIMA, Prophet)
Trend, Seasonality, and Noise

Capstone Project

Real-world End-to-End ML Project
Problem Definition, Data Collection, EDA, Model Building, Evaluation
Model Deployment Basics

Machine Learning

Machine Learning

Machine Learning

Course Curriculum

Introduction to Machine Learning

Python for Machine Learning

Data Preprocessing \& Feature Engineering

Exploratory Data Analysis (EDA)

Supervised Learning Algorithms

Unsupervised Learning Algorithms

Model Evaluation and Validation

Model Tuning \& Optimization

Introduction to Deep Learning

Natural Language Processing (NLP)

Time Series Analysis

Capstone Project

For More Detail Contact Now

About Us

Courses

Cloud Monks Skill Innovations Pvt Ltd

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Machine Learning

Machine Learning

Machine Learning

Machine Learning

Course Curriculum

Introduction to Machine Learning

Python for Machine Learning

Data Preprocessing \& Feature Engineering

Exploratory Data Analysis (EDA)

Supervised Learning Algorithms

Unsupervised Learning Algorithms

Model Evaluation and Validation

Model Tuning \& Optimization

Introduction to Deep Learning

Natural Language Processing (NLP)

Time Series Analysis

Capstone Project

For More Detail Contact Now

About Us

Courses

Cloud Monks Skill Innovations Pvt Ltd

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