60 Days of Machine Learning

Week 1: Python Basics

Day 1: Python Basics - Part 1 ✅️

• Learn about data types: integers, floats, strings, and booleans.
• Understand variables and assignment.
• Work with basic arithmetic and operators.
• Use comparison and logical operators.

Day 2: Python Basics - Part 2 ✅️

• Explore lists, tuples, and sets.
• Learn about dictionaries and how to use them.
• Perform basic operations with these data structures.

Day 3: Python Basics - Part 3 ✅️

• Understand control flow: if, else, elif statements.
• Learn about loops: for and while loops.
• Explore break and continue statements.
• Introduction to functions and their definitions.

Day 4: Intermediate Python - Part 1 ✅️

• Learn about first-class functions and closures.
• Understand global and non-local variables.
• Explore the use of magic methods and tuple unpacking.
• Learn about static variables and methods.

Day 5: Intermediate Python - Part 2✅️

• Dive into lambda functions.
• Explore inheritance and polymorphism. ✔️
• Learn about error and exception handling. ✔️
• Introduction to Python’s garbage collection and debugging. ✔️

Day 6: Advanced Python - Part 1

• Learn about decorators and their uses.
• Understand memoization using decorators.
• Explore generators and yield statements.

Day 7: Advanced Python - Part 2

• Work with collections: OrderedDict, defaultdict, namedtuple.
• Explore coroutines and asynchronous programming.

Week 2: Mathematics and Statistics

Day 8: Statistics for Data Science

• Understand descriptive statistics: mean, median, mode.
• Learn about probability and distributions.
• Introduction to hypothesis testing.

Day 9: Mathematics for ML - Part 1 ✅️

• Learn linear algebra basics: vectors, matrices, and operations.
• Understand eigenvalues and eigenvectors.

Day 10: Mathematics for ML - Part 2 ✅️

• Explore calculus: derivatives and integrals.
• Understand the concept of gradient descent.

Week 3: Data Handling with Python

Day 11: Pandas - Part 1 ✅️

• Learn the basics of Pandas: Series and DataFrame.
• Understand data indexing and selection.
• Perform data manipulation and cleaning.

Day 12: Pandas - Part 2 ✅️

• Explore advanced data operations: merging, joining, and concatenation.
• Learn about groupby operations and pivot tables.

Day 13: Numpy Basics ✅️

• Understand Numpy arrays and their operations.
• Perform mathematical and statistical operations with Numpy.

Day 14: Data Preprocessing - Part 1 ✅️

• Learn about data cleaning and handling missing values.
• Perform feature scaling and normalization.

Day 15: Data Preprocessing - Part 2 ✅️

• Explore data transformation techniques.
• Learn about feature engineering and selection.

Week 4: Introduction to Machine Learning

Day 16: Introduction to ML

• Understand the basics of machine learning.
• Learn about supervised and unsupervised learning.

Day 17: Linear Regression

• Learn about simple linear regression.
• Understand the mathematics behind linear regression.
• Implement linear regression in Python.

Day 18: Multiple Linear Regression

• Explore multiple linear regression.
• Implement multiple linear regression in Python.

Day 19: Polynomial Regression

• Understand polynomial regression.
• Implement polynomial regression in Python.

Day 20: Support Vector Regression

• Learn about support vector machines for regression.
• Implement support vector regression in Python.

Week 5: Machine Learning Algorithms

Day 21: Decision Tree Regression

• Understand decision trees and how they work.
• Implement decision tree regression in Python.

Day 22: Random Forest Regression

• Learn about ensemble methods and random forests.
• Implement random forest regression in Python.

Day 23: Logistic Regression

• Understand logistic regression for classification.
• Implement logistic regression in Python.

Day 24: K-Nearest Neighbors (KNN)

• Learn about KNN algorithm.
• Implement KNN for classification and regression.

Day 25: Support Vector Machines (SVM)

• Understand SVM for classification.
• Implement SVM in Python.

Day 26: Naive Bayes

• Learn about Naive Bayes classifiers.
• Implement Naive Bayes in Python.

Week 6: Clustering and Dimensionality Reduction

Day 27: K-Means Clustering

• Understand the k-means clustering algorithm.
• Implement k-means clustering in Python.

Day 28: Hierarchical Clustering

• Learn about hierarchical clustering methods.
• Implement hierarchical clustering in Python.

Day 29: Principal Component Analysis (PCA)

• Understand PCA for dimensionality reduction.
• Implement PCA in Python.

Day 30: Advanced Clustering Techniques

• Explore DBSCAN and other advanced clustering algorithms.
• Implement advanced clustering techniques in Python.

Week 7: Deep Learning Basics

Day 31: Introduction to Neural Networks

• Understand the basics of neural networks.
• Learn about activation functions and layers.

Day 32: Deep Learning with TensorFlow - Part 1

• Introduction to TensorFlow.
• Build and train a basic neural network.

Day 33: Deep Learning with TensorFlow - Part 2

• Learn about convolutional neural networks (CNNs).
• Implement a CNN for image classification.

Day 34: Recurrent Neural Networks (RNN)

• Understand RNNs and their applications.
• Implement an RNN for sequence data.

Day 35: Long Short Term Memory Networks (LSTM)

• Learn about LSTM networks.
• Implement LSTM for time series prediction.

Day 36: Transfer Learning

• Understand transfer learning and its benefits.
• Implement transfer learning for image classification.

Week 8: Natural Language Processing (NLP)

Day 37: Introduction to NLP

• Understand the basics of NLP.
• Learn about text preprocessing techniques.

Day 38: Sentiment Analysis

• Implement sentiment analysis using machine learning.

Day 39: Text Classification

• Learn about text classification techniques.
• Implement text classification using machine learning.

Day 40: Named Entity Recognition (NER)

• Understand NER and its applications.
• Implement NER using machine learning.

Day 41: Topic Modeling

• Learn about topic modeling techniques.
• Implement topic modeling using LDA.

Day 42: Advanced NLP with BERT

• Introduction to BERT and transformers.
• Implement text classification using BERT.

Week 9: Model Evaluation and Tuning

Day 43: Model Evaluation Techniques

• Learn about different model evaluation metrics.
• Understand confusion matrix, precision, recall, F1-score.

Day 44: Cross-Validation

• Understand the concept of cross-validation.
• Implement cross-validation techniques.

Day 45: Hyperparameter Tuning

• Learn about hyperparameter tuning methods.
• Implement grid search and random search for hyperparameter tuning.

Day 46: Model Selection

• Understand model selection criteria.
• Learn about bias-variance tradeoff.

Week 10: Time Series Analysis

Day 47: Introduction to Time Series Analysis

• Understand time series data and its components.
• Learn about stationary and non-stationary time series.

Day 48: Time Series Forecasting - Part 1

• Implement moving average and exponential smoothing.

Day 49: Time Series Forecasting - Part 2

• Learn about ARIMA models.
• Implement ARIMA for time series forecasting.

Week 11: Advanced Machine Learning Topics

Day 50: Ensemble Methods

• Understand ensemble learning.
• Implement bagging and boosting algorithms.

Day 51: Gradient Boosting Machines (GBM)

• Learn about GBM and its variants.
• Implement GBM in Python.

Day 52: XGBoost

• Introduction to XGBoost.
• Implement XGBoost for regression and classification.

Day 53: LightGBM

• Learn about LightGBM.
• Implement LightGBM for regression and classification.

Week 12: Special Topics and Final Projects

Day 54: Reinforcement Learning - Part 1

• Introduction to reinforcement learning.
• Understand the basics of Q-learning.

Day 55: Reinforcement Learning - Part 2

• Implement a simple reinforcement learning algorithm.

Day 56: Computer Vision with OpenCV

• Introduction to OpenCV.
• Implement basic image processing tasks.

Day 57: Advanced Computer Vision

• Learn about object detection and segmentation.
• Implement YOLO or SSD for object detection.

Day 58: Final Project - Part 1

• Start working on a comprehensive data science project.
• Perform data collection and preprocessing.

Day 59: Final Project - Part 2

• Build and evaluate machine learning models.
• Fine-tune and optimize your models.

Day 60: Final Project - Part 3

• Present your final project.
• Create a project report and share your findings.

Machine Learning Algorithms

Supervised Learning

• Regression
  • Linear Regression ✅
  • Polynomial Regression ✅
  • Ridge/Lasso Regression ✅
• Classification
  • Logistic Regression ✅
  • Naive Bayes
  • K-NN ✅
  • SVM ✅
  • Decision Trees ✅

Unsupervised Learning

• Clustering
  • K-Means✅
  • Mean Shift ✅
  • DBSCAN (Density-Based Spatial Clustering Of Applications With Noise)
  • Agglomerative
  • Fuzzy C-Means
• Dimensionality Reduction
  • PCA
  • LDA
  • t-SNE
  • SVD
  • LSA
• Association Rule Learning
  • Apriori
  • FP Growth
  • Eclat

Ensemble Learning

• Bagging
  • Random Forest
• Boosting
  • AdaBoost
  • Gradient Boosting
  • XGBoost
  • LightGBM
  • CatBoost
• Stacking

Neural Networks and Deep Learning

• Perceptrons
• Autoencoders
• Convolutional Neural Networks (CNNs)
  • DCNN
• Recurrent Neural Networks (RNNs)
  • LSTM
  • GRU
  • seq2seq
• Generative Adversarial Networks (GAN)
• Deep Belief Networks (DBN)
• Long Short-Term Memory (LSTM)
• Gated Recurrent Unit (GRU)

Reinforcement Learning

• Q-Learning
• SARSA
• Deep Q-Networks (DQN)
• A3C
• Genetic Algorithm