AI & Machine Learning – Complete Self-Learning Syllabus
This is an introduction to AI/ML Self Learning. If you are a new comer or don't have enough foundation in basic coding please cover the C, Java & OOPS fundamentals and have a strong foundation in basic programming first.
Orientation & Foundations
What is Artificial Intelligence (AI)
What is Machine Learning (ML)
What is Deep Learning (DL)
AI vs ML vs DL (clear comparison)
ML vs Data Science vs Data Analyst
Traditional Programming vs Machine Learning
Why Machine Learning is used
When Machine Learning should NOT be used
Real-world ML systems overview
1. Introduction to Machine Learning
1.1 What is Machine Learning?
Definition of Machine Learning
Subset of Artificial Intelligence
Uses data to solve tasks
Learns patterns from past data
How machines learn from data
ML models
Trained using data
Based on probability, statistics, and linear algebra
Predictive capability
Learns from historical data
Predicts future outcomes
Examples of ML in daily life
1.2 Why Machine Learning?
Handles large amounts of data
Structured data
Rows and columns
Organized datasets
Unstructured data
Text, images, audio
Learns automatically without explicit rules
Improves performance with experience
1.3 Real-World Data Examples
E-commerce data
Amazon sales reports
Thousands to lakhs of rows
Customer datasets
Age, gender, occupation
Location, marital status
Learning from past data to predict outcomes
2. Types of Machine Learning
2.1 Supervised Learning
Uses labeled data
Target variable is known
Types
Regression
Classification
2.2 Unsupervised Learning
Uses unlabeled data
Finds hidden patterns automatically
Types
Clustering
Dimensionality Reduction
2.3 Semi-Supervised Learning
Partially labeled data
Used when labeling is expensive
2.4 Reinforcement Learning
Agent
Environment
Reward
Policy
Applications of RL
2.5 Comparison of ML Types
Differences between supervised, unsupervised, semi-supervised, and RL
Use-cases for each type
3. Applications of Machine Learning
Recommendation systems
Image recognition
Speech recognition
Natural Language Processing (NLP)
Fraud detection
Healthcare
Manufacturing & quality control
Autonomous systems
Chatbots & virtual assistants
4. Traditional Programming vs Machine Learning
4.1 Traditional Programming
Rules + Data → Output
Fixed logic
No learning from data
Example
If email contains "win money" → Spam
4.2 Machine Learning
Data + Output → Model
Model learns rules automatically
Improves with experience
Examples
Spam detection
Diabetes prediction
5. Python Programming for Machine Learning
5.1 Python Basics
Python introduction
Variables
Keywords
Comments
Indentation
5.2 Data Types
Integer
Float
String
Boolean
Type conversion
5.3 Data Structures
List
Tuple
Set
Dictionary
Differences between data structures
Use-cases in ML
5.4 Operations on Data Structures
Insert
Update
Delete
Indexing
Slicing
5.5 Operators
Arithmetic operators
Relational operators
Logical operators
Assignment operators
Membership operators
5.6 Inbuilt Functions
len()
sum()
min()
max()
sorted()
type()
5.7 Control Statements
if
if-else
elif
for loop
while loop
break
continue
pass
Difference between for and while loop
5.8 Input & Output
input()
print()
Formatted output
5.9 Logical Practice Problems
Prime number
Palindrome
Armstrong number
Fibonacci series
Factorial
Unique elements in list
Frequency counting
Largest and smallest element
Pattern problems
Array and string problems
6. Python Libraries for Machine Learning
6.1 NumPy
Arrays
Array operations
Vectorized operations
Mathematical functions
6.2 Pandas
Series
DataFrame
Data loading
Data cleaning
Data manipulation
6.3 Data Visualization
Matplotlib
Seaborn
Basic plots
Line plot
Bar plot
Histogram
Box plot
6.4 Scikit-Learn
Introduction to scikit-learn
Datasets
Model training
Model prediction
Model evaluation
7. Machine Learning Workflow
7.1 End-to-End ML Pipeline
Problem understanding
Data collection
Data preprocessing
Exploratory Data Analysis (EDA)
Feature engineering
Feature selection
Feature scaling
Train-test split
Model selection
Model training
Model evaluation
Model optimization
Model saving
Testing with unseen data
Model deployment (basic understanding)
7.2 Data Preprocessing
Handling missing values
Handling outliers
Removing duplicates
Encoding categorical variables
Feature scaling
Normalization
Standardization
7.3 Exploratory Data Analysis (EDA)
Dataset overview
Data types and shape
Statistical summary
Data distribution
Central tendency
Data spread
Correlation analysis
Visualization
8. Statistics for Machine Learning
8.1 Descriptive Statistics
Mean
Median
Mode
Range
Variance
Standard deviation
Quartiles
Interquartile range (IQR)
8.2 Statistical Equations
Mean formula
Variance formula
Standard deviation formula
Quartile calculation
8.3 Usage of Statistics in ML
Mean for normalization
Median for outlier handling
Variance for feature importance
Standard deviation for scaling
Quartiles for data distribution analysis
9. Mathematics for Machine Learning
9.1 Vectors
Vector definition
Vector representation
Vector addition
Scalar multiplication
Dot product
Vector usage in ML
9.2 Matrices
Matrix representation
Matrix addition
Matrix multiplication
Matrix transpose
Identity matrix
Matrix usage in ML
10. Probability for Machine Learning
10.1 Probability Basics
Probability definition
Sample space
Events
10.2 Types of Events
Independent events
Dependent events
Conditional probability
10.3 Probability in Machine Learning
Classification problems
Prediction confidence
Risk and uncertainty
Naive Bayes intuition
10.4 Advanced Probability Topics
Bayes theorem
Random variables
Probability distributions
Normal distribution
Binomial distribution
11. Machine Learning Algorithms
11.1 Supervised Learning Algorithms
Linear Regression
Multiple Linear Regression
Logistic Regression
K-Nearest Neighbors (KNN)
Decision Tree
Random Forest
Support Vector Machine (SVM)
Naive Bayes
11.2 Regression
Continuous value prediction
Examples
House price prediction
Temperature prediction
Stock prices
Regression evaluation metrics
Mean Squared Error (MSE)
R² Score
11.3 Classification
Categorical prediction
Binary and multi-class classification
Evaluation metrics
Accuracy
Precision
Recall
F1-Score
Confusion Matrix
Classification Report
11.4 Unsupervised Learning Algorithms
K-Means Clustering
Hierarchical Clustering
Principal Component Analysis (PCA)
12. Model Evaluation & Optimization
Training data vs testing data
Overfitting
Underfitting
Bias vs variance
Cross-validation
Hyperparameter tuning
13. Machine Learning Projects
Student performance prediction
House price prediction
Customer segmentation
Spam detection
Recommendation system (basic)
Quality defect prediction
End-to-end ML project workflow
14. MLOps & Deployment (Introductory)
Model saving and loading
Basic deployment concepts
ML lifecycle overview
Monitoring models
15. Ethics & Responsible AI
Bias in ML models
Fairness
Explainability
Privacy concerns
16. Career Roadmap for Machine Learning
16.1 Learning Path
Learn fundamentals
Practice coding
Understand algorithms
16.2 Portfolio Building
Well-organized GitHub
Clean notebooks
Documentation
Results and explanations
16.3 Career Growth
Resume optimization
LinkedIn optimization
Networking
Internships
Freelancing
Interview preparation
17. Summary
AI, ML, and DL foundations
ML types and algorithms
Python, math, statistics, and probability
End-to-end ML workflow
Projects, ethics, deployment, and career guidance
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