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Mentee Question
1. I’m from a tier-3 college with no guidance or referrals—how can I strategically plan my journey to crack into a Product-Based Company offering 16+ LPA, starting from scratch? 2. Can you suggest the most effective way to build strong fundamentals in DSA, System Design, and Development Projects that product companies value? 3. How can I stand out and get shortlisted for top tech internships if I don’t have a strong college brand or referrals? AND Which companies or platforms should I actively target/apply to for internships that can lead to a 16+ LPA full-time offer later? 4. What kind of resume, GitHub profile, and LinkedIn presence should I work on to stand out without having big college branding or internal referrals?
Mentors Answer
Answered By Mentor Ajeya Jois
Phase 1: Foundation Building (Weeks 1–4)
Goal: Understand the core fundamentals and build a strong base.
Step 1: Learn Prerequisites
- Mathematics
- Programming (Python)
Phase 2: Core Machine Learning (Weeks 5–10)
Goal: Learn ML theory and implement key algorithms.
Step 2: Learn Supervised Learning
- Regression
- Classification
Step 3: Learn Unsupervised Learning
- Clustering
- Dimensionality Reduction
Step 4: Model Evaluation & Tuning
- Train-Test Split, Cross-validation
- Metrics: Accuracy, Precision, Recall, F1, ROC-AUC
- Hyperparameter Tuning: Grid Search, Random Search
Phase 3: Data Skills (Weeks 11–14)
Goal: Master data preprocessing, cleaning, visualization.
Step 5: Data Preprocessing
- Handling Missing Values
- Encoding Categorical Variables
- Feature Scaling (Standardization, Normalization)
Step 6: Data Visualization & EDA
Phase 4: Advanced ML & Special Topics (Weeks 15–20)
Goal: Learn techniques to enhance model performance and real-world applications
Step 7: Feature Engineering & Selection
Step 8: Model Deployment (Real World Ready)
Phase 5: Deep Learning & Neural Networks (Weeks 21–28)
Goal: Dive into neural networks and computer vision/NLP basics.
Step 9: Deep Learning Fundamentals
Step 10: Deep Learning Libraries
Phase 6: Projects & Real-World Applications
Step 11: Build End-to-End ML Projects
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