ML PATHWAY

1.
Week 1: Python & Basic Libraries (10-14 hours total)
• Objective: Get comfortable with Python, NumPy, Pandas, and Matplotlib.
• Plan:
o Day 1-4: Python basics (variables, functions, loops, lists, dictionaries)
o Day 5-7: Dive into NumPy (arrays, matrix operations), Pandas (dataframes), and
Matplotlib (plotting).
• Resources:
o Codecademy or Python Crash Course by Corey Schafer on YouTube.
o NumPy/Pandas tutorials (official documentation or Kaggle).
o Matplotlib tutorials (YouTube or Matplotlib official site).
2. Week 2: Linear Algebra Basics (7-10 hours total)
• Objective: Learn basic concepts like vectors, matrices, dot products, and matrix
multiplication (not too deep, just intuition).
• Plan:
o Day 1-4: Watch tutorials on linear algebra (Khan Academy or 3Blue1Brown).
o Day 5-7: Implement operations using NumPy and practice with small exercises.
• Resources:
o 3Blue1Brown series on linear algebra (YouTube).
o Practice with Khan Academy.
3. Week 3: Probability & Statistics Fundamentals (7-10 hours total)
• Objective: Understand mean, variance, standard deviation, probability distributions, and

basic statistical concepts.
• Plan:
o Day 1-4: Learn statistics (mean, median, variance, normal distribution, probability).
o Day 5-7: Implement basic statistical concepts in Python with Pandas.
• Resources:
o Khan Academy: Basic statistics and probability courses.
o Python implementation using Pandas (datasets for practice).
4. Week 4: Introduction to Machine Learning (10-12 hours total)

• Objective: Understand the high-level concepts of machine learning: supervised vs
unsupervised learning, classification vs regression.
• Plan:
o Day 1-3: Read about types of machine learning, basic algorithms like linear
regression, logistic regression, k-nearest neighbors (KNN).
o Day 4-7: Try small examples using Scikit-learn.
• Resources:
o Google ML Crash Course or Andrew Ng’s Machine Learning Specialization (Week 1).
o Scikit-learn tutorials for implementing linear regression, KNN.
Phase 2: Core Machine Learning Concepts & Projects (3-4 months)
Goal: Learn core machine learning algorithms, build intuition, and apply them on real-world
datasets.
Daily Commitment: 2 hours/day
1. Week 5-6: Supervised Learning (Linear Models)
• Objective: Learn about linear regression, logistic regression, and their implementations.
• Plan:
o Day 1-7: Study linear regression (theory + code using Scikit-learn), metrics like MSE,
R-squared.
o Day 8-14: Study logistic regression (theory + code). Implement binary classification
tasks.
• Project Idea: Predict house prices using a dataset (Kaggle).
2. Week 7-8: Classification Algorithms
• Objective: Learn decision trees, random forests, and support vector machines (SVM).
• Plan:
o Day 1-7: Study decision trees, random forests (theory + code).
o Day 8-14: Study SVMs and implement using Scikit-learn.
• Project Idea: Classify images (MNIST digits dataset or other beginner datasets from Kaggle).
3. Week 9-10: Unsupervised Learning (Clustering and Dimensionality Reduction)
• Objective: Learn k-means clustering, PCA, and t-SNE.

• Plan:
o Day 1-7: Study k-means clustering (theory + code).
o Day 8-14: Study PCA, t-SNE for dimensionality reduction and data visualization.
• Project Idea: Cluster customers based on purchasing patterns or reduce dimensions of image
data.
4. Week 11-12: Model Evaluation & Cross-Validation
• Objective: Learn how to evaluate models, cross-validation, and hyperparameter tuning.
• Plan:
o Day 1-4: Learn about evaluation metrics (accuracy, precision, recall, F1 score).
o Day 5-7: Learn cross-validation techniques.
o Day 8-12: Learn hyperparameter tuning (GridSearchCV).
• Project Idea: Improve the performance of a classification or regression model through cross-
validation and tuning.
5. Week 13-16: Neural Networks and Introduction to Deep Learning
• Objective: Get a basic understanding of neural networks and deep learning.
• Plan:
o Day 1-7: Study the basics of neural networks (how they work,
forward/backpropagation).
o Day 8-14: Start implementing simple neural networks using TensorFlow or PyTorch.
o Day 15-21: Learn about deep learning architectures like CNNs and RNNs (at a high
level).
• Project Idea: Build an image classifier using a small neural network.
Phase 3: Advanced Concepts & Real-World Projects (2-3 months)
Goal: Build complex projects, refine your skills, and dive into advanced areas like deep learning,
NLP, or reinforcement learning.
Daily Commitment: 2-3 hours/day
1. Week 17-20: Advanced Deep Learning (Optional)
• Objective: Explore advanced neural networks, CNNs (for image processing), and RNNs (for
sequences).
• Plan:
o Learn convolutional neural networks (CNNs) for image-related tasks.
o Learn recurrent neural networks (RNNs) for sequential data (text, time series).
• Project Idea: Build an image classifier (using CNNs) or a text generator (using RNNs).
2. Week 21-24: Final Real-World Projects
• Objective: Apply all the concepts to build projects from scratch.
• Plan:
o Pick 1-2 complex datasets from Kaggle or create your own projects.
o Work on end-to-end projects (from data preprocessing to model deployment).
• Project Ideas:
o Sentiment analysis using NLP.
o Build a recommendation system (movies, products).
o Create a deep learning model for image recognition (advanced datasets).

ML PATHWAY

Uploaded by

Copyright:

Available Formats

ML PATHWAY

Uploaded by

Document Information

Copyright

Available Formats

Share this document

Share or Embed Document

Sharing Options

Did you find this document useful?

Is this content inappropriate?

Copyright:

Available Formats

ML PATHWAY

Uploaded by

Copyright:

Available Formats

1.

Week 1: Python & Basic Libraries (10-14 hours total)

• Objective: Get comfortable with Python, NumPy, Pandas, and Matplotlib.

o Day 1-4: Python basics (variables, functions, loops, lists, dictionaries)

o Codecademy or Python Crash Course by Corey Schafer on YouTube.

o NumPy/Pandas tutorials (official documentation or Kaggle).

o Matplotlib tutorials (YouTube or Matplotlib official site).

2. Week 2: Linear Algebra Basics (7-10 hours total)

o Day 1-4: Watch tutorials on linear algebra (Khan Academy or 3Blue1Brown).

o 3Blue1Brown series on linear algebra (YouTube).

o Practice with Khan Academy.

3. Week 3: Probability & Statistics Fundamentals (7-10 hours total)

• Objective: Understand mean, variance, standard deviation, probability distributions, and

o Day 5-7: Implement basic statistical concepts in Python with Pandas.

o Khan Academy: Basic statistics and probability courses.

o Python implementation using Pandas (datasets for practice).

4. Week 4: Introduction to Machine Learning (10-12 hours total)

o Day 4-7: Try small examples using Scikit-learn.

o Scikit-learn tutorials for implementing linear regression, KNN.

Phase 2: Core Machine Learning Concepts & Projects (3-4 months)

Daily Commitment: 2 hours/day

1. Week 5-6: Supervised Learning (Linear Models)

• Project Idea: Predict house prices using a dataset (Kaggle).

2. Week 7-8: Classification Algorithms

o Day 1-7: Study decision trees, random forests (theory + code).

o Day 8-14: Study SVMs and implement using Scikit-learn.

3. Week 9-10: Unsupervised Learning (Clustering and Dimensionality Reduction)

• Objective: Learn k-means clustering, PCA, and t-SNE.

o Day 1-7: Study k-means clustering (theory + code).

4. Week 11-12: Model Evaluation & Cross-Validation

• Objective: Learn how to evaluate models, cross-validation, and hyperparameter tuning.

o Day 5-7: Learn cross-validation techniques.

o Day 8-12: Learn hyperparameter tuning (GridSearchCV).

5. Week 13-16: Neural Networks and Introduction to Deep Learning

• Objective: Get a basic understanding of neural networks and deep learning.

• Project Idea: Build an image classifier using a small neural network.

Phase 3: Advanced Concepts & Real-World Projects (2-3 months)

Daily Commitment: 2-3 hours/day

1. Week 17-20: Advanced Deep Learning (Optional)

o Learn convolutional neural networks (CNNs) for image-related tasks.

2. Week 21-24: Final Real-World Projects

• Objective: Apply all the concepts to build projects from scratch.

o Work on end-to-end projects (from data preprocessing to model deployment).

o Sentiment analysis using NLP.

o Build a recommendation system (movies, products).

o Create a deep learning model for image recognition (advanced datasets).

You might also like