Machine learning is a rapidly growing field within the broader domain of **Artificial Intelligence**. It involves developing algorithms that can automatically learn patterns and insights from data without being explicitly programmed. Machine learning has become increasingly popular in recent years as businesses have discovered its potential to drive innovation, improve decision-making, and gain a competitive advantage.

## 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**.

When it comes to finding a job in machine learning, some companies are more actively hiring than others. Some of the top companies in this field include **Google, Microsoft, Amazon, IBM,** and **Facebook**. These companies are known for their innovative use of machine learning and AI and offer exciting opportunities for those looking to advance their careers.

Whether you’re just starting to explore machine learning or you’re already well-versed in the subject, there are many resources available to help you learn and grow in this exciting field. This guide is intended to serve as a roadmap for your journey, providing an overview of the basics and pointing you in the direction of more advanced topics and resources.

## 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:

- System of Linear Equation
- Matrix Operation
- Properties of Matrix
- Solving Linear Equations using Gaussian Elimination
- LU Decomposition of Linear Equation
- Row Echelon Form
- Determinant
- Eigenvalues and Eigenvectors
- Eigenspace
- Orthogonal and Orthonormal Vectors
- Eigen Decomposition
- Diagonalization
- Singular Value Decomposition â€” Implementation
- Matrix Approximation
- Vector Operations
- Differentiation
- Minima and Maxima
- Area Under Curve

## 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:

- Mean, Standard Deviation, and Variance â€” Implementation
- Descriptive Statistics
- Descriptive and Inferential Statistics
- Probability Theory and Distribution
- Normal Distribution
- Binomial Distribution
- Uniform Distribution

- Types of Sampling Distribution
- Degrees of Freedom
- Z-Test
- t-Test
- Chi-Square Test

- Linear Regression
- Sample Error and True Error
- Bias Vs Variance and Its Trade-Off
- Hypothesis Testing
- Confidence Intervals
- Correlation and Covariance
- Correlation Coefficient
- Covariance Matrix
- Pearson Correlation
- Spearmanâ€™s Rank Correlation Measure
- Kendall Rank Correlation Measure
- Robust Correlations
- Maximum Likelihood Estimation

## 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.

To learn Python programming, you should have the knowledge of the following topics:

- Python Basics
- Python Data Structures
- Python Programming Fundamentals
- Working with Data in Python

You can also explore the some of the best courses to learn Python and Machine Learning

## 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.

#### Libraries for Data Handling and Visualization in Python

#### Data Preprocessing:

- Introduction to Data Preprocessing
- Data Cleaning
- Missing Values
- Inconsistent Data
- Data Transformation
- Data Reduction
- Feature extraction
- Feature Transformation
- Feature Selection

#### Data Visualization:

- Introduction to data visualization
- Exploratory Data Analysis
- Descriptive Statistical Analysis
- Data Visualization with Different Charts
- Visualization using Matplotlib
- Advanced visualization using Matplotlib
- Visualization using Seaborn

In conclusion, data preprocessing and visualization are crucial steps in the machine learning pipeline, and days 28-45 of the “100 days of Machine Learning” challenge focus on these fundamental topics. Preprocessing helps in preparing data for analysis by handling missing values, outliers, and duplicates, normalizing data through scaling and standardization, and transforming data by encoding categorical variables, selecting features, and reducing dimensionality. Visualization, on the other hand, helps in gaining insights from data by representing it through charts and graphs, and tools such as Matplotlib and Seaborn can be used to create a variety of visualizations. By mastering these techniques, learners can gain a solid foundation in data preprocessing and visualization, which will help them in their future machine-learning projects.

## 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:

- What is Machine Learning?
- Types of Machine Learning
- ML â€“ Applications
- Getting Started with Classification
- Basic Concept of Classification
- Types of Regression Techniques
- Classification vs Regression
- ML | Types of Learning â€“ Supervised Learning
- Multiclass classification using scikit-learn
- Gradient Descent:
**Linear Regression**- Introduction to Linear Regression
- Gradient Descent in Linear Regression
- Mathematical explanation for Linear Regression working
- Normal Equation in Linear Regression
- Linear Regression (Python Implementation)
- Univariate Linear Regression in Python
- Multiple Linear Regression using Python
- Locally weighted Linear Regression
- Python | Linear Regression using sklearn

**Logistic Regression****Naive Bayes**Classifiers**Support Vector Machine****Decision Tree****Random Forest**

For the complete Tutorial, refer to –

Machine Learning Tutorial

## 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.

- Bias Variance Trade-Off
- Model evaluation techniques
- Importance of Splitting the data into training, validation, and testing
- Cross-validation techniques
- ML Evaluation Metrics
- Classification Evaluation Metrics
- Accuracy Score
- Precision, recall, and F1 score
- Confusion Matrix
- ROC curve

- Regression Evaluation Metrics
- Mean Absolute Error
- Mean Squared Error
- Mean Absolute Percentage Error
- R2 Score

- Hyperparameter tuning

In conclusion, days 77-84 of the “100 days of Machine Learning” challenge focus on the crucial steps of evaluating and selecting the best model for a given problem. Evaluation is the process of measuring a model’s performance using various metrics such as precision, recall, and F1 score, and techniques such as cross-validation and ROC curves can be used for this purpose. Model selection involves choosing the best model from a set of candidate models, and hyperparameter tuning can be used to optimize the performance of these models. Techniques such as GridSearchCV and RandomizedSearchCV can be used to automate the hyperparameter tuning process. By mastering these techniques, learners can develop the ability to evaluate and select the best model for a given problem, which is a crucial skill in the field of machine learning.

## 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.

#### Regression-Based Projects

#### Classification Based Projects

## 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:

- Biological Neurons Vs Artificial Neurons
- Single Layer Perceptron
- Multi-Layer Perceptron
- Forward and backward propagation
- Feed-forward neural networks
- Neural Network layers
- Introduction to Activation Function
- Types Of Activation Function
- Understanding Activation Functions in Depth
- Cost function in Neural Networks
- How does Gradient Descent work
- Vanishing or Exploding Gradients Problems
- Choose the optimal number of epochs
- Fine-Tuning & Hyperparameters

**Deep learning** utilizes neural networks to extract knowledge from data and has produced remarkable results in several complex tasks. To develop a comprehensive understanding of this area, learners need to study the architecture of neural networks. By mastering these concepts, learners can gain a solid foundation in deep learning and neural networks, which will enable them to work on exciting and challenging projects in this field.

## 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.

In conclusion, the journey of 100 days of Machine Learning will be an incredible learning experience. Through this process, one can gain a strong foundation in Machine Learning and its applications in various fields. The article covered several topics: Machine Learning, Data Preparation, Regression, Classification, Clustering, Natural Language Processing, and Deep Learning, etc.

The skills acquired during the 100 days of Machine Learning are valuable in today’s world, where data is becoming increasingly important in decision-making processes across industries. By going through this article, you will take this important step towards being proficient in Machine Learning and are now better equipped to tackle complex problems in their respective fields. Overall, the 100 days of Machine Learning will be an excellent investment in terms of time and effort, and the you can expect to reap the rewards of their hard work for years to come.

## FAQs

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

**Answer:**

There are several courses present online on different platforms Such as

GeeksforGeeks,Udemy,Coursera,Udacity, etc. We would recommend you visitComplete Machine Learning & Data Science Programin order to get basic to Advance level knowledge of Machine learning.

### Q2: What are the different types of machine learning approaches?

**Answer:**

There are primarily three types of machine learning:

SupervisedUnsupervisedReinforcement Learning

**Q3: What are the benefits of learning machine learning?**

**Answer:**

Learning machine learning can lead to several benefits, including career opportunities in

data scienceandAI, developing skills inprogramming,data analysis, andstatistics, and contributing to the development of innovative technologies.

### Q4: What are some common applications of machine learning?

**Answer:**

Machine learning can be used in various applications such as

image recognition,natural language processing,recommendation systems,speech recognition,fraud detection, andautonomous vehicles.

**Q5: What is the Future scope of Machine Learning?**

**Answer:**

The future scope of Machine learning is promising as there are various industries that it is transforming day by day by adding innovative technological advancements such as

Healthcare,Finance,Manufacturing,Education, andTransportation.