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