PART 1: Introduction to Python

• Introduction to Python programming language and its role in machine learning and deep learning.
• Python syntax and basic programming concepts, including variables, data types, operators, and control structures (if statements, loops).
• Introduction to functions and how to define and call them.
• Working with Python libraries such as NumPy, Pandas, and Matplotlib for data manipulation and visualization.
• Understanding the importance of Python for data analysis, machine learning, and deep learning tasks.
• Hands-on exercises and coding examples to practice Python syntax, data manipulation using libraries, and basic programming concepts.
• Guided exercises to load and manipulate data using NumPy arrays, perform basic statistical operations, and create plots using Matplotlib.

PART 2: Data Pre-processing

• Importance of data pre-processing in machine learning and deep learning.
• Data cleaning, handling missing values, and outlier detection.
• Feature scaling, encoding categorical variables, and feature transformation techniques.

PART 3: Introduction to Machine Learning

• Overview of Machine Learning concepts and algorithms
• Supervised vs. Unsupervised Learning
• Model evaluation and validation techniques
• Introduction to Scikit-learn library

1. Supervised Learning Algorithms

• Simple linear regression and multiple linear regression for continuous variable prediction.
• Logistic regression for binary classification.
• K-Nearest Neighbors (K-NN) algorithm for classification.
• Decision tree classification and random forest classification.
• Ensemble Learning: Bagging and Boosting

B) Unsupervised Learning Algorithms

• Introduction to clustering algorithms.
• K-means clustering and hierarchical clustering techniques.
• Dimensionality Reduction Techniques
• Principal Component Analysis (PCA)
• Evaluation metrics for clustering algorithms.

PART 4: Introduction to Deep learning

• Introduction to TensorFlow /Keras
• Basics of Neural Networks and their Architecture.
• Building blocks of Deep Learning: Layers, Activation Functions, Optimizers
• Building and training models for Classification and Regression tasks
• Building model of Image Classification
• Model for Sequence Data

PART 5: Industry based capstone projects

• Real-world capstone projects applying machine learning and deep learning techniques.