DATA SCIENCE & MACHINE LEARNING
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DATA SCIENCE & ML Course
Data Science and Machine Learning are among the most popular and high-demand fields in the technology industry today.
They simplify complex decision-making and power innovation across every sector.
Data Science and Machine Learning cover both data management and predictive analytics, allowing you to build intelligent systems that learn and improve over time.
The role of a Data Scientist and Machine Learning Engineer is highly sought after, with a dramatic surge in global demand and continuous growth expected in the future.
This course will help you master Data Analysis, Machine Learning Algorithms, and Predictive Modeling by first covering the fundamentals of Statistics, Python Programming, and Data Processing Techniques.
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DATA SCIENCE & ML COURSE CONTENT
INCLUDE ALL MODULES
Introduction
Data Science Overview
– Introduction to Data Science.
– The significance of data in decision-making.
– Understanding the end-to-end data science workflow (from data collection to model deployment).
Data Science Maths In-Depth
1. Introduction to Statistics
* Population & Sample
* Descriptive vs Inferential Statistics
* Basic Statistical Measures
2. Measure of Central Tendency (Median, Mean, Mode)
* Measures of Variability
* Percentage, Percentiles, and Quartiles
3. Probability
* Probability Distribution
* Normal Distribution
* Advanced Statistical Concepts
4. Covariance and Correlation
* Central Limit Theorem
* Hypothesis Testing
Machine Learning Complete
1. Introduction to Machine Learning (ML)
2. Roadmap to Learning Machine Learning
3. Types of Data and Variables in ML
4. Data Cleaning:
* Identifying and Handling Missing Values
* One Hot Encoding & Dummy Variables
* Label Encoding
* Ordinal Encoding
* Outlier Detection and Removal
* Feature Scaling (Standardization and Normalization)
* Handling Duplicate Data
* Data Type Transformation
5. Feature Selection Techniques:
* Backward Elimination (using mixed)
* Forward Elimination (using mixed)
Supervised Learning in ML
1. Train Test Split in Dataset
2. Regression Analysis:
* Linear Regression Algorithm (Simple Linear)
* Multiple Linear Regression
* Polynomial Regression
3. Cost Function in Regression
4. R Squared Score & Adjusted R Squared in Regression Analysis
Classification in ML
1. Classification
2. Logistic Regression:
* Binary Classification (Practical)
* Binary Classification with Multiple Inputs (Practical)
* Binary Classification with Polynomial Inputs (Practical)
* Multiclass Classification (Practical)
3. Confusion Matrix
4. Imbalanced Dataset Handling
5. Naive Bayes Algorithm
Non-Linear Supervised Algorithm in ML
1. Non-Linear Supervised Algorithms:
* Decision Tree (Classification)
* Decision Tree (Regression)
* K-Nearest Neighbors (Classification)
2. Hyperparameter Tuning
3. Cross-Validation
4. Unsupervised Learning
Clustering in ML
1. Clustering
2. K-means Clustering
3. Hierarchical Clustering
4. DBSCAN Clustering Algorithm
5. Silhouette Score
Association in ML
1. Association
2. Association Rule Learning
3. Apriori Algorithm
4. Frequent Pattern Growth Algorithm
Ensemble Learning in ML
1. Ensemble Learning
2. Max Voting, Averaging & Weighted Average Voting
* Practical Implementation for Regression
* Practical Implementation for Classification
3. Bagging (with Bagging meta-estimator and Random forest)
Deep Learning & AI Complete
1. Deep Learning Overview:
* Introduction to Deep Learning
* Neurons, Neural Networks, and Types of Deep Learning Networks
2. Perceptrons:
* Single Layer Perceptron
* Multilayer Perceptron (Artificial Neural Networks)
3. Training Process:
* Forward Propagation and Backpropagation
* Activation Functions
* Loss Functions
* Optimizers
4. Practical Applications
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