Additionally, you can learn Ploty and Tableau if you want.

4. Statistics

Statistics for machine learning come as a significant tool that studies this data for recognizing certain patterns. It helps you find unseen patterns by providing a proper direction for utilizing, analyzing, and presenting the raw data that is successfully implemented in fields like computer vision and speech analysis.

Descriptive Statistics

• Continuous and Discrete Functions
• Probability Distribution
• Gaussian Normal Distribution
• Measure of Frequency and Central Tendency
• Measure of Dispersion
• Skewness and Kurtosis
• Normality Test
• Regression Analysis
• Linear and Non-Linear Relationship with Regression
• ANOVA
• Homoscedasticity
• Goodness of Fit

Inferential Statistics

• t-Test, z-Test
• Hypothesis Testing
• Type I and Type II errors
• One-way and Two way ANOVA
• Chi-Square Test
• Implementation of continuous and categorical data

5. Machine Learning

To become proficient in machine learning algorithms, the most effective approach is to utilize the Scikit-Learn framework. Scikit-Learn provides a wealth of pre-defined algorithms that can be easily implemented by creating class objects. Familiarizing yourself with these algorithms is essential, especially those falling under the categories of Supervised and Unsupervised Machine Learning:

1. Linear Regression
2. Logistic Regression
3. Decision Tree
4. Gradient Descent
5. Random Forest
6. Ridge and Lasso Regression
7. Naive Bayes
8. Support Vector Machine
9. KMeans Clustering

Other important things to know

• Principal Component Analysis
• Recommender systems
• Predictive Analytics
• Exploratory Data Analysis

6. Natural Language Processing

Natural Language Processing (NLP) is of paramount importance for Machine Learning (ML) engineers for several reasons. NLP enables ML engineers to work with human language data, which is prevalent in various applications and industries.

• Handling Unstructured Text DataSentiment analysis
• Text Classification and Sentiment Analysis
• Named Entity Recognition (NER)
• Text preprocessing
• Text Generation and Language Translation
• Topic Modeling
• Machine Translation, BLEU Score
• Summarization, ROUGE Score
• Language Modeling, Perplexity
• Building a text classifier
• Speech Recognition

7. Deep Learning

The best way to master deep learning algorithms is to work with TensorFlow or PyTorch.

• Neural networks basics
• Activation functions
• Backpropagation algorithm
• Popular deep learning frameworks: TensorFlow or PyTorch
• Convolutional Neural Networks (CNN) for computer vision
• Recurrent Neural Networks (RNN) for sequential data
• Generative Adversarial Networks (GAN) for data generation

8. Computer Vision

Computer vision is a fascinating field that involves teaching computers to understand and interpret visual information from images and videos, just like the human visual system does.

• Working with OpenCV
• Understanding Pretrained models like AlexNet, ImageNet, ResNet.
• Neural Networks
• Building a perceptron
• Building a single-layer neural network
• Building a deep neural network
• Recurrent neural network for sequential data analysis
• Image Content Analysis
• Operating on images using OpenCV-Python
• Detecting edges

9. MLOps

You can master any one of the cloud services providers from AWS, GCP, and Azure. You can switch easily once you understand one of them. We will focus on AWS - Amazon Web Services first

• Working with Deep Learning on AWS
• Amazon Rekognition - Image Applications
• Amazon Textract - Extract Text
• Amazon Transcribe - Speech to Text
• AWS Polly - Voice Analysis
• Amazon Lex - Natural Language Understanding
• Amazon SageMaker - Building and deploying models
• Deploy ML models using Flask

10. Git & GitHub

Git and GitHub are essential tools in the field of Machine Learning (ML) for version control, collaboration, and sharing ML projects with the community.

• Understanding Git
• Commands and How to commit your first code?
• How to use GitHub?
• How to make your first open-source contribution?
• How to work with a team? - Part 1
• How to create your stunning GitHub profile?
• How to build your own viral repository?
• Building a personal landing page for your Portfolio for FREE
• How to grow followers on GitHub?
• How to work with a team? Part 2 - issues, milestone and projects

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