Share Description
Become an ML Engineer with Scaler's Machine Learning Course
- Study the essential Machine learning topics like Supervised Learning, Unsupervised Learning, Reinforcement Learning, and much more from scratch.
- Build your portfolio by working on real-world business projects
- Placement support + Ongoing guidance after course completion + Opportunity to interact with Machine Learning industry experts
Syllabus :
1. Introduction to Programming
- Decision trees & control
- Binary number system
- Strings
- Arithmetic operators
- Loops
2. Programming - Intermediate
- Introduction to Number systems and Bit Manipulations
- Time Complexity Analysis
- Arrays Techniques
- Basic Maths for Programming
- Sorting and Hashing Libraries
- Recursion
- OOPS
3. Statistical Analysis & Data Analytics
- Python, Jupyter, Numpy, Pandas
- Git, Linux Terminal, File I/O
- Statistics, Probability, Linear Algebra
- Distributions, Sampling, Hypothesis Testing
- Databases, SQL, Index, Partition, Schema
- Web API, Scraping, Automation, Flask
4. Machine Learning
- Recommender system
- Bayesian Machine Learning
- EDA, Data wrangling, Feature Engineering
- Ensemble Learning
- Supervised Learning
- Unsupervised Learning
- Predictive Modeling & Time Series Forecasting
- Decision Trees
5. ML Engineering - Deep Learning & Big Data
- Reinforcement Learning, Q-learning
- Keras, PyTorch, TensorFlow
- Information Extraction, Entity Recognition
- Neural Networks, NLP, Computer Vision
- Research Papers in Deep Learning
- Warehouse - AWS S3, HDFS, HBase, NoSQL
- BERT, Building Chatbots
- Analysis - Airflow, PySpark, Hive, YARN
6. Programming - Advanced
- Time Complexity, Arrays, Strings, Binary Search, 2 Pointers, Recursion, Hashing, Sorting, Bit manipulation
- Stacks, Queues, Linked Lists, Trees, Tries, Heap
- Greedy, DP, Graphs
- DB and System Design
