Matplotlib Graph Basics Format Strings in Plots Label Parameters, Legend Bar Chart, Pie Chart, Histogram, Scatter Plot 4 | Statistics Descriptive Statistics Measure of Frequency and Central Tendency Measure of Dispersion Probability Distribution Gaussian Normal Distribution Skewness and Kurtosis Regression Analysis Continuous and Discrete Functions Goodness of Fit Normality Test ANOVA Homoscedasticity Linear and Non-Linear Relationship with Regression Inferential Statistics t-Test z-Test Hypothesis Testing Type I and Type II errors t-Test and its types One way ANOVA Two way ANOVA Chi-Square Test Implementation of continuous and categorical data

5 | Machine Learning The best way to master machine learning algorithms is to work with the ScikitLearn framework. Scikit-Learn contains predefined algorithms and you can work with them just by generating the object of the class. These are the algorithm you must know including the types of Supervised and Unsupervised Machine Learning: Linear Regression Logistic Regression Decision Tree Gradient Descent Random Forest Ridge and Lasso Regression Naive Bayes Support Vector Machine KMeans Clustering Other Concepts and Topics for ML Measuring Accuracy Bias-Variance Trade-off Applying Regularization Elastic Net Regression Predictive Analytics Exploratory Data Analysis 6 |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 AWSAmazon Web Services first Deploy ML models using Flask Amazon LexNatural Language Understanding AWS PollyVoice Analysis Amazon TranscribeSpeech to Text Amazon TextractExtract Text Amazon RekognitionImage Applications Amazon SageMakerBuilding and deploying models Working with Deep Learning on AWS 7| Natural Language Processing If you are interested in working with Text, you should do some of the work an NLP Engineer do and understand the working of Language models. Sentiment analysis POS Tagging, Parsing, Text preprocessing Stemming and Lemmatization Sentiment classification using Naive Bayes TF-IDF, N-gram, Machine Translation, BLEU Score Text Generation, Summarization, ROUGE Score Language Modeling, Perplexity Building a text classifier Identifying the gender

8 | Computer Vision To work on image and video analytics we can master computer vision. To work on computer vision we have to understand images. PyTorch Tensors Understanding Pretrained models like AlexNet, ImageNet, and ResNet. Neural Networks Building a perceptron Building a single-layer neural network Building a deep neural network Recurrent neural network for sequential data analysis Convolutional Neural Networks Understanding the ConvNet topology Convolution layers Pooling layers Image Content Analysis Operating on images using OpenCV-Python Detecting edges Histogram equalization Detecting corners Detecting SIFT feature points 9 | Data Visualization with Tableau How to use it Visual Perception What is it, How it works, Why Tableau Connecting to Data Building charts Calculations Dashboards Sharing our work Advanced Charts, Calculated Fields, Calculated Aggregations Conditional Calculation, Parameterized Calculation