180-day AI and Machine Learning course from scratch
AI and Machine Learning
180-day AI and Machine Learning course from scratch is an ambitious and exciting project! The goal is to ensure that each day is engaging, practical, and builds on the previous day's learning. Below is a structured outline for the course, divided into phases, with each day focusing on a single concept. The course will include theory, practical examples, and code snippets where applicable.
Phase 1: Foundations of AI and Python Programming (Days 1-30)
Goal: Build a strong foundation in Python programming and understand the basics of AI and Machine Learning.
Days 1-10: Python Basics
Day 3: Loops (for, while).
Day 5: Lists in Python
Day 6: Tuples in Python
Day 7: Sets in Python
Day 8: Dictionaries in Python
Day 9: File handling in Python.
Day 10: Error handling (try, except).
Days 11-20: Data Manipulation and Visualization
Day 11: Introduction to NumPy arrays.
Day 12: Array operations in NumPy.
Day 13: Introduction to Pandas DataFrames.
Day 14: Data cleaning with Pandas.
Day 15: Data visualization with Matplotlib.
Day 16: Advanced visualization with Seaborn.
Day 18: Data aggregation and grouping.
Day 19: Mini-project: Analyze a dataset (e.g., Titanic dataset).
Day 20: Recap and Q&A session.
Days 21-30: Introduction to AI and Machine Learning
Day 21: What is AI? History and applications.
Day 22: Types of Machine Learning: Supervised, Unsupervised, Reinforcement.
Day 23: Introduction to Scikit-Learn.
Day 24: Data preprocessing: Scaling and normalization.
Day 25: Train-test split.
Day 26: Linear regression theory and implementation.
Day 27: Evaluation metrics: MSE, RMSE, R².
Day 28: Logistic regression theory and implementation.
Day 29: Mini-project: Predict house prices using linear regression.
Day 30: Recap and Q&A session.
Phase 2: Core Machine Learning Concepts (Days 31-90)
Goal: Dive deeper into supervised and unsupervised learning algorithms.
Days 31-40: Supervised Learning
Day 31: Decision trees theory.
Day 32: Decision trees implementation.
Example:
from sklearn.tree import DecisionTreeClassifier
Day 33: Random forests theory.
Day 34: Random forests implementation.
Example:
from sklearn.ensemble import RandomForestClassifier
Day 35: Support Vector Machines (SVM) theory.
Day 36: SVM implementation.
Example:
from sklearn.svm import SVC
Day 37: K-Nearest Neighbors (KNN) theory.
Day 38: KNN implementation.
Example:
from sklearn.neighbors import KNeighborsClassifier
Day 39: Mini-project: Classify iris flowers using KNN.
Day 40: Recap and Q&A session.
Days 41-50: Unsupervised Learning
Day 41: Clustering: K-Means theory.
Day 42: K-Means implementation.
Example:
from sklearn.cluster import KMeans
Day 43: Hierarchical clustering theory.
Day 44: DBSCAN theory and implementation.
Example:
from sklearn.cluster import DBSCAN
Day 45: Dimensionality reduction: PCA theory.
Day 46: PCA implementation.
Example:
from sklearn.decomposition import PCA
Day 47: t-SNE theory and implementation.
Example:
from sklearn.manifold import TSNE
Day 48: Mini-project: Customer segmentation using K-Means.
Day 49: Recap and Q&A session.
Day 50: Mid-course review and project planning.
Days 51-60: Advanced Topics
Day 51: Gradient Descent and optimization.
Day 52: Regularization: L1 and L2.
Day 53: Hyperparameter tuning: Grid Search.
Example:
from sklearn.model_selection import GridSearchCV
Day 54: Hyperparameter tuning: Random Search.
Example:
from sklearn.model_selection import RandomizedSearchCV
Day 55: Ensemble methods: Bagging and Boosting.
Day 56: XGBoost theory and implementation.
Example:
import xgboost as xgb
Day 57: LightGBM theory and implementation.
Example:
import lightgbm as lgb
Day 58: CatBoost theory and implementation.
Example:
from catboost import CatBoostClassifier
Day 59: Mini-project: Predict loan defaults using XGBoost.
Day 60: Recap and Q&A session.
Phase 3: Deep Learning and Advanced AI (Days 61-120)
Goal: Understand and implement deep learning models.
Days 61-70: Neural Networks Basics
Day 61: Introduction to neural networks.
Day 62: Perceptron and activation functions.
Day 63: Backpropagation and gradient descent.
Day 64: Introduction to TensorFlow and Keras.
Example:
import tensorflow as tf
Day 65: Building a simple neural network.
Example:
model = tf.keras.Sequential([tf.keras.layers.Dense(10)])
Day 66: Training and evaluating a neural network.
Example:
model.compile(); model.fit()
Day 67: Overfitting and dropout.
Example:
tf.keras.layers.Dropout(0.2)
Day 68: Mini-project: MNIST digit classification.
Day 69: Recap and Q&A session.
Day 70: Mid-phase review.
Days 71-90: Convolutional Neural Networks (CNNs)
Day 71: Introduction to CNNs.
Day 72: Convolution and pooling layers.
Day 73: Building a CNN with Keras.
Example:
model.add(tf.keras.layers.Conv2D(32, (3, 3)))
Day 74: Transfer learning with pre-trained models.
Example:
from tensorflow.keras.applications import VGG16
Day 75: Data augmentation.
Example:
tf.keras.preprocessing.image.ImageDataGenerator
Day 76: Mini-project: Image classification with CNNs.
Day 77: Recap and Q&A session.
Day 78: Introduction to object detection.
Day 79: YOLO and SSD models.
Day 80: Mini-project: Object detection in images.
Phase 4: Specialized Topics and Capstone Project (Days 121-180)
Goal: Explore advanced topics and apply knowledge to a capstone project.
Days 121-150: Advanced Topics
Days 121-130: Natural Language Processing (NLP).
Days 131-140: Reinforcement Learning.
Days 141-150: Generative Adversarial Networks (GANs).
Days 151-180: Capstone Project
Days 151-160: Project planning and data collection.
Days 161-170: Model building and training.
Days 171-180: Evaluation, deployment, and presentation.
This course is designed to be interactive, practical, and rewarding. Each day builds on the previous one, ensuring a deep understanding of AI and Machine Learning concepts. Happy learning! 🚀
Dear Team
Kindly restart this 180 day course... It's a treasure to have this.