Curriculum
10 Sections
43 Lessons
Lifetime
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Module 1: Introduction to AI & ML
2 Items
Understand AI, ML, Deep Learning, their evolution, applications, careers and responsible use.
1.1
1.1 Fundamentals of Artificial Intelligence & Machine Learning
16 Minutes
1.2
1.2 AI Applications, Career Opportunities & Responsible AI
15 Minutes
Module 2: Python for AI & ML
8 Items
Learn Python basics, Colab, functions, NumPy and Pandas for AI, ML and data handling.
2.1
2.1 Introduction to Python for AI & ML
18 Minutes
2.2
2.2 Variables, Data Types & Operators
9 Minutes
2.3
2.3 Control Structures: Loops & Conditions
2.4
2.4 Functions & Modules in Python
12 Minutes
2.5
2.5 Introduction to NumPy & Pandas
9 Minutes
2.6
2.6 Data Manipulation & Analysis with Pandas
8 Minutes
2.7
2.7 Data Handling Basics with Pandas
10 Minutes
2.8
2.8 Data Filtering & Selection
5 Minutes
Module 3: Data & Data Preprocessing
9 Items
Prepare data using encoding, feature engineering, missing values, scaling and outlier detection.
3.1
3.1 Understanding Data Types & Categorical Data
17 Minutes
3.2
3.2 Outlier Detection & Visualization
10 Minutes
3.3
3.3 Feature Engineering & Data Encoding
15 Minutes
3.4
3.4 Data Formatting & Type Conversion
10 Minutes
3.5
3.5 String Manipulation
4 Minutes
3.6
3.6 Handling Missing Values
4 Minutes
3.7
3.7 Date Formatting & Extraction
3 Minutes
3.8
3.8 Feature Scaling: Standard & MinMax Scaling
7 Minutes
3.9
3.9 Data Decomposition & Aggregation
10 Minutes
Module 4: Exploratory Data Analysis (EDA)
5 Items
Explore, analyse and visualise data using statistics, correlations, Matplotlib and Seaborn.
4.1
4.1 Introduction to Exploratory Data Analysis
12 Minutes
4.2
4.2 Correlation Analysis & Feature Relationships
14 Minutes
4.3
4.3 Data Visualization with Matplotlib & Seaborn
14 Minutes
4.4
4.4 Data Distribution Analysis 4.5 Descriptive Statistics
6 Minutes
4.5
4.5 Descriptive Statistics
8 Minutes
Module 5: Machine Learning Fundamentals
1 Item
Learn how machines learn, key learning paradigms and the fundamentals of supervised learning.
5.1
5.1 Machine Learning Fundamentals & Learning Paradigms
Module 6: Supervised Learning
6 Items
Learn regression, classification, KNN, Decision Trees, Random Forest, SVM and practical modelling.
6.1
6.1 Introduction to Supervised Learning & Classification
14 Minutes
6.2
6.2 Regression Analysis: Concepts & Techniques
17 Minutes
6.3
6.3 Regression Practical: Diabetes Dataset
10 Minutes
6.4
6.4 Logistic Regression & Sigmoid Function
6 Minutes
6.5
6.5 Logistic Regression Practical
15 Minutes
6.6
6.6 Supervised Learning Algorithms Practical
8 Minutes
Module 7: Unsupervised Learning
6 Items
Explore clustering, K-Means, Hierarchical Clustering, DBSCAN, PCA and dimensionality reduction.
7.1
7.1 Clustering Concepts & Unsupervised Learning Algorithms
11 Minutes
7.2
7.2 Principal Component Analysis (PCA) Practical
8 Minutes
7.3
7.3 K-Means Clustering & Distance Metrics
10 Minutes
7.4
7.4 K-Means Clustering Implementation
6 Minutes
7.5
7.5 Hierarchical Clustering
8 Minutes
7.6
7.6 DBSCAN Practical
6 Minutes
Module 8: Model Evaluation & Optimization
2 Items
Evaluate and improve ML models using key metrics, cross-validation and hyperparameter tuning.
8.1
8.1 Model Evaluation Metrics & Hyperparameter Tuning
14 Minutes
8.2
8.2 Model Evaluation, Cross-Validation & Tuning Practical
18 Minutes
Module 9: Introduction to Deep Learning
3 Items
Learn neural networks, activation functions, TensorFlow, Keras, backpropagation and ANN concepts.
9.1
9.1 Neural Networks & Deep Learning Fundamentals
24 Minutes
9.2
9.2 Deep Learning Project: Higher Education Recommendation System
11 Minutes
9.3
9.3 TensorFlow & Keras Practical
10 Minutes
Module 10: AI Applications & Deployment Basics
1 Item
Explore AI applications, NLP, chatbots, computer
10.1
10.1 Real-World AI Applications & Model Deployment Basics
6 Minutes
Artificial Intelligence and Machine Learning – Hindi
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