Day 28 – 45: Data Preprocessing and Visualization

ML in the Job Industry

If you’re interested in pursuing a career in machine learning, you may be wondering about the salary and career options available to you. Machine learning professionals are in high demand and can earn competitive salaries. According to Glassdoor, the average base pay for a machine learning engineer in the United States is around $114,000 per year, with some earning well over $150,000 per year. The field also offers a variety of career paths, including roles such as Data Scientist, Machine Learning Engineer, and AI researcher....

Day 1 – 10: Linear Algebra

The first 10 days of your Machine Learning journey should focus on understanding the basics of Linear Algebra. You should start by learning about the different types of Linear equations, matrices, mathematical operations, and their applications. You should also familiarize yourself with the key concepts and terminologies used in Linear algebra. Here are the key topics to be covered in Linear Algebra:...

Day 11 – 20: Statistics

After a decent understanding of linear algebra and its operations, it’s time to move forward one step ahead with Statistics in order to deal with data. Having good knowledge of Stats will eventually help in Data analysis, modeling, and evaluation in your journey of machine learning. There are numerous applications of statistics in machine learning such as Data exploration and preprocessing, Feature selection, Model selection and evaluation, uncertainty estimation, etc. So let’s dive into the core of statistics:...

Day 21 – 27 Python Programming

For the implementation of machine learning techniques, one needs to know a language that the device can understand and here Python comes to play. Whenever there is a need of selecting a language for programming,  the first language that pops out is PYTHON. It can be used in Machine Learning in several ways such as Data preprocessing and manipulation, building ML models, Data visualization, etc....

Day 28 – 45: Data Preprocessing and Visualization

It is imperative to comprehend the significance of Data preprocessing and visualization. These procedures aid in readying your data for analysis and detecting patterns and trends that can be instrumental in shaping your models. It is advisable to acquaint yourself with techniques such as Data cleansing, Data normalization, and Data transformation. Additionally, learning how to use visualization tools such as Matplotlib and Seaborn to represent your data and gain valuable insights from it is crucial....

Day 46 – 76: Introduction to Machine Learning and its Algorithms

The next few days of your machine learning journey should focus on understanding the basics of machine learning. You should start by learning about the different types of machine learning and their applications. You should also familiarize yourself with the key concepts and terminologies used in machine learning. After that, it is time to delve into the realm of algorithms. There exist several machine learning algorithms to opt from, and the selection of an algorithm hinges on the nature of the problem you seek to resolve.Here are the key topics to be covered in the Introduction to Machine Learning and its Algorithms:...

Day 77 – 84: Evaluation and Model Selection

Once you have trained your models, you need to evaluate their performance and select the best one for your problem....

Day 85 – 94: ML Projects

Now, it’s time to get some hands-on experience with machine learning. So, here are some projects mentioned below that will help you to understand the functionality and practical implementation of machine learning techniques....

Day 95 – 100: Introduction to Deep Learning

Deep learning is a specialized area of machine learning that deploys neural networks to assimilate knowledge from data. Its impact has been transformative in numerous domains such as computer vision, natural language processing, and speech recognition. To gain a comprehensive understanding, it is advisable to study  in the final days of your ML journey:...

Conclusion:

Machine learning is a rapidly growing field with immense potential to revolutionize almost everything around us. By grasping the fundamentals of machine learning, data preprocessing, and visualization, one can start creating their own machine learning models to tackle real-world situations and provide effective self-sustaining solutions for them. There are numerous algorithms available, from linear regression to deep learning, and selecting the appropriate one depends on the nature of the problem you are attempting to solve....

FAQs

Q1: From where I should learn Machine learning?...