## Table of Contents

- Additional Resources for learning:

Machine learning is in great demand in near future and people are looking for courses and details all over. This is for aspiring machine learning Engineers. After completing this course, start applying for jobs, doing contract work, start your own machine consulting group, or just keep on learning.

Remember to believe in your ability to learn. You can learn machine learning, you will learn ML, and if you stick to it, eventually you will master it.

People are born with intrinsic motivation, self -esteem, dignity, curiosity to learn, joy in learning

I am sharing this information which is already present online on different sources and created this consolidated post for Machine learning. Below you can see the machine learning market size and growth in Industry.

**Month 1: Machine learning**

**Week 1: Linear Algebra**

- This introduces the “Essence of linear algebra” series, aimed at animating the geometric intuitions underlying many of the topics taught in a standard linear algebra course.
__watch__ - This is a basic subject in matrix theory and linear algebra. Emphasis is given to topics that will be useful in other disciplines, including systems of equations, vector spaces, determinants, eigenvalues, similarity, and positive definite matrices. This course is generated under MIT open courseware(OCW) watch
- One of the most interesting topics to visualize in Linear Algebra is Eigenvectors and Eigenvalues. Here you will learn how to easily calculate them and how they are applicable and particularly interesting when it comes to machine learning implementations.
__watch__

**Week 2: Calculus**

The goal here is to make calculus feel like something that you yourself could have discovered watch

**Week 3: Probability**

- Probability – The Science of Uncertainty and Data

Build foundational knowledge of data science with this introduction to probabilistic models, including random processes and the basic elements of statistical inference. watch - Probability and statistics – Khan Academy

Build foundational knowledge of data science with this introduction to probabilistic models, including random processes and the basic elements of statistics. watch

**Week 4: Algorithms**

- Learn about the core principles of computer science: algorithmic thinking and computational problem-solving.
__watch__ - Technical interviews follow a pattern. If you know the pattern, you’ll be a step ahead of the competition. This course will introduce you to common data structures and algorithms in Python.
__watch__

**Month 2: Machine learning**

**Week 1: Learn python for ****Data scienc**e

- Explore a variety of datasets, posing and answering your own questions about each. You’ll be using the Python libraries NumPy, Pandas, and Matplotlib.
__watch__ - In this python for data science video you will learn end to end on data science with python. So this python data science tutorial will help you learn various python concepts and machine learning algorithms to get you started in this technology.
__watch__

**Math of Intelligence**

This course will go over a very popular optimization technique called gradient descent to help us predict how many calories a cyclist would burn given just the distance traveled. We’ll also follow the story of 2 data scientists as they attempt to find the Higgs-Boson (God particle) via anomaly detection. __watch__

**Introduction to Tensorflow**

This will teach you a brief introduction to TensorFlow for beginners. It is the most popular machine learning library. watch

**Week 2: Introduction to Machine learning**

- This class will teach you the end-to-end process of investigating data through a machine learning lens, and you’ll apply what you’ve learned to a real-world data set.
__watch__ - Google Developed this fast-paced Machine learning crash course
__learn__

**Week 3-4: Machine learning projects**

A curated list of practical deep learning and machine learning project ideas. Great ideas related to machine learning projects.open

**Month 3: Deep learning**

**Week 1: Introduction to Deep Learning**

Deep Learning is an important subfield of Artificial Intelligence (AI) that connects various topics like Machine Learning, Neural Networks, and Classification. The field has advanced significantly over the years due to the works of giants like Andrew Ng, Geoff Hinton, Yann LeCun, Adam Gibson, and Andrej Karpathy. Many companies have also invested heavily in Deep Learning and AI research – Google with DeepMind and its Driverless car, nVidia with CUDA and GPU computing, and recently Toyota with its new plan to allocate one billion dollars to AI research. Learn

**Week 3: Deep learning by fastai**

You might be surprised by what you *don’t* need to become a top deep learning practitioner. You need one year of coding experience, a GPU, and appropriate software and that’s it. You don’t need much data, you don’t need university-level math, and you don’t need a giant data center. learn

**Week 3-4: Machine learning projects**

A curated list of practical deep learning and machine learning project ideas. Great ideas related to machine learning projects. __work__

**Additional Resources for learning:**

People in ML to __follow on Twitter__

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