Target Language: French
Current Level: Beginner
Learning Goal: Conversational fluency
Available Time Per Day: 1 hour
Learning Style: Visual
Timeframe: 6 months
Daily Routine (1 hour):
- 10 min: Vocabulary building with flashcards (use visuals + images): learn 10 new words/phrases connected to daily life themes. Use apps like Quizlet with picture mode or Anki with images.
- 15 min: Watch a beginner-level French video (see weekly recommendations), do shadowing by repeating every sentence aloud, focusing on pronunciation and intonation.
- 10 min: Dictation exercise: Write down key sentences or short segments from the video you watched. Use subtitles if needed, then re-watch without subtitles to compare.
- 10 min: Speaking practice: summarize aloud (or record yourself) the content/viewed video in simple French sentences, even if very basic. Use a recording app and listen back.
- 15 min: Reading + writing: Read a related transcript or short article (simple text about daily life), write 3–5 sentences summarizing main points or your thoughts using new vocabulary.
Weekly Video Recommendations and Structure:
Weeks 1–4:
- Channels: “Français Authentique” (beginner playlist), “Learn French with Alexa” (beginner lessons), “Easy French” (street interviews with subtitles)
- Video style: Simple dialogues, slow speech, daily life topics
- Task focus: Shadowing + dictation + simple oral summarizing
Weeks 5–8:
- Channels: “Coffee Break French” (video lessons), “Learn French with Vincent” (elementary lessons), “InnerFrench” (easy podcasts/videos with transcripts)
- Video style: More natural pace, short stories and everyday conversations
- Task focus: Shadowing longer sentences, dictation of phrases, writing short paragraph summaries
Weeks 9–16:
- Channels: “Journal en français facile” (news), “France 24 – Simplified French news clips”, French vlogs for learners (Easy French)
- Video style: Current events, simple news, cultural topics, lifestyle vlogs
- Task focus: Dictation of news excerpts, oral summaries without notes, start watching without subtitles for short clips
Weeks 17–24:
- Channels: Native content—short French dramas, TED Talks in French with subtitles, French YouTubers (with slower speech, especially educational channels like “Cyprien” or “Norman Fait des Vidéos” with subtitles)
- Video style: Natural, faster speech, cultural content, humor
- Task focus: Shadow longer phrases and dialogues, write detailed video summaries, practice real-time oral summaries, watch without subtitles with active note-taking
Checkpoints:
- After 1 month: Watch a 2–3 minute beginner video without subtitles. Write down or speak aloud main ideas. Check comprehension with transcript.
- After 3 months: Watch a 5-minute intermediate video (news or vlog) without subtitles. Summarize orally or in writing in French. Record yourself and compare with transcript if available.
- After 6 months: Watch a 10-minute native content video without subtitles. Write a comprehensive summary and record an oral reaction. Have a tutor or language partner provide feedback.
Motivation strategies:
- Track daily progress with a visible calendar or language journal including video titles and vocabulary learned.
- Reward yourself weekly when meeting shadowing/dictation goals with French media-related treats (e.g., a French movie night).
- Join French learner communities (Reddit r/French, language exchange apps) to share video summaries and get feedback.
- Alternate fun cultural videos (music videos, travel vlogs) with lessons to keep content engaging.
- Set mini conversational challenges: e.g., narrate your day or describe a picture in French, inspired by vocabulary from your videos.
This structured, video-powered approach aligned with your visual learning style and daily hour will build solid vocabulary, listening, speaking, reading, and writing skills progressively toward conversational fluency.
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That's great that you're diving into Machine Learning (ML)! Since you're a 2nd-year Computer Science student, you likely have a solid foundation in programming and math, which sets you up well for understanding ML concepts. Here’s a structured set of resources tailored to your current level and long-term goal of becoming proficient in ML:
1. Foundational Math & Programming Refresher
Before diving deep into ML, ensure your math (especially linear algebra, probability, and calculus) and Python programming skills are strong.
Books:
- Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
(Great for building ML-relevant math intuition) - Think Python by Allen B. Downey (if you want to strengthen Python)
- Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
Online Courses:
2. Introductory Machine Learning
Once comfortable with math and basic Python, start with beginner-friendly but comprehensive ML courses and books.
Courses:
- Andrew Ng’s Machine Learning Course (Coursera)
(Classic and highly recommended, covers algorithms and intuition with Octave/Matlab) - Google’s Machine Learning Crash Course
(Hands-on with TensorFlow and practical exercises)
- Andrew Ng’s Machine Learning Course (Coursera)
Books:
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
(Practical, Python-based, great for hands-on learning) - Pattern Recognition and Machine Learning by Christopher M. Bishop
(More theoretical, great next step once comfortable)
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
3. Intermediate to Advanced Topics
After grasping fundamentals, move to deep learning, NLP, reinforcement learning, and other subfields.
Courses:
- Deep Learning Specialization by Andrew Ng (Coursera)
- Fast.ai Practical Deep Learning for Coders (Very practical and code-first)
- CS231n: Convolutional Neural Networks for Visual Recognition (Stanford)
(Excellent for computer vision and deep learning)
Books:
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (freely available online)
- Reinforcement Learning: An Introduction by Richard Sutton and Andrew Barto
4. Practice Platforms
Practical experience is key:
- Kaggle (https://www.kaggle.com/)
(Competitions and datasets to build projects and learn from others) - Google Colab (https://colab.research.google.com/)
(Run Python notebooks in the cloud with free GPU)
5. YouTube Channels & Websites
- 3Blue1Brown — Excellent math intuition videos (especially the Neural Networks series)
- StatQuest with Josh Starmer — Clear explanations of statistics and ML concepts
- Sentdex — Practical Python ML tutorials
- Two Minute Papers — Good for catching up on cutting-edge research in digestible videos
- Distill.pub — Interactive, visual essays on ML topics
6. Additional Tips
- Build Projects: Nothing beats learning by doing. Try implementing classic algorithms (linear regression, decision trees) from scratch.
- Join Communities: Reddit’s r/MachineLearning, Stack Overflow, and Discord servers for ML learners.
- Research Papers: As you advance, start reading papers from arXiv; tools like Papers with Code link research to implementations.
If you want, I can suggest a detailed study plan week-by-week or help you pick resources for specific ML areas like NLP or Computer Vision. Just let me know!