Mlops Training
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Mlops Introduction (152:53)
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python basics | List and nested list in python | Data analysis and pandas (267:16)
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Numpy Array | Image processing | Image cropping | Data analysis basiccs and linere regression (275:45)
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Regression model concept | Linear regression | Image processing (247:13)
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Linear regression | Python basics | Image processing (250:49)
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Linear regression , Prediction, Ml model, Matplotlib, Sklearn, Seaborn, Loss function, Metrices, Feature elimination (265:37)
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Linear regression | Feature selection | multi linear regression | computer vision (232:48)
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Computer vision ( opencv2 ) | Pre-trained model | Haarcascade model | Ip webcam integration | Multi linear regression (263:24)
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Multi linear regression | Feature selection | Feature engineering concept | Categorial variable | Dummy variable | Multi co-linearity, (233:42)
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Dimensity reduction | feature selection | feature extraction | linear regression | p-value and significance level | OLS and backward elimination and wrapper methond (163:54)
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neural learning / Dl | image processsing | OLS | feature selection | gretl | graphs | object detection | face recognization | docker | linux basics (271:08)
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Tensorflow | Scalar | lazy execution | Eager execution | Perceptron model | Gradient descent (200:01)
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Graph | Tensorflow | DL | Neural Network | Artificial neural network | Activation function | CNN concept (145:55)
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Activation function | DL | Regression | Classification | Feed forward Forward Propogation | Back propagation | Keras (267:57)
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Neural network | Data visualization | graph | folium | Object detection | CNN | YOLO-Setup and Use | Optimizer | Initializer (257:09)
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Data visualization | Classification | Histogram | Graph | Heatmap | Regression | Binary classification (265:34)
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Binary Classification | Model using sigmoid function | Logistic regression | visuals, searborn | graph | bar graph | Discrete variable | frequency distribution table | One hot encoding | Feature Engineering | Missing value resolving using feature engineering | Imputation (123:46)
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Visualization | Exploratory data analysis | One hot label encoding | Nan | Binary class | logistic regression | confusion matrix (118:54)
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Graph | Seaborn | folium | Seaborn | Pandas | Plotly | Cuffling (115:47)
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29. Logistic regression | Lazy learning | K-nearest neighbours algo | Pattern recognizing KNN (115:42)
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30. Supervised learning | Unsupervised learning | Clustering | Soft clustering ( soft kmeans ) (139:24)
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31. Feature scaling | Standardization | Normalization | Kmeans | Iterations (136:03)
Docker Training Session
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Docker Basic Commands | Launching container | Ephemeral storage | Persistent storage | Mounting volume | Configuring the Web Server | Curl Command (112:44)
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Docker Basic Commands | Launching container | Port Number | Configure Container as Webserver and Database Server | Environmental Variables (114:19)
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Word Press | MySQL | Set-up Three Tier Architecture | Patting | Hosting a Webpage | Container Linking (122:16)
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Docker Networking | NAT | Networking Basics | SDN | Network Infrastructure | Bridge (129:29)
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Runtime | Plugins | NAT | Bridge Network Interface | SDN | DHCP | DNS | Subnet | Gateway | IPAM | Custom Network Infrastructure (135:17)
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Create Own Images | Commit | Code | Clone a running Container| Keywords | Non-interactive Commands | Interactive Commands | Docker File | Build Time | Run Time (134:27)
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Docker File | Docker Commit Command | Run Keyword | CMD Keyword | Run Time | Build Time | DFOREGROUND Option | Share Image | Docker Save Command | Docker Load Command (88:16)
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Docker Registry | Docker Hub | Different ways of creating image | ENTRYPOINT keyword in docker file | Install Python Software in conatiner | -t Option RUN command | Copy files in container | Creating image | Passing python program files as arguments | MAINTAINER keyword | Docker login | Docker push (121:52)
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PID | Why Docker is Superfast? | Need of OS | Process | Nested Process | Pgrep Command | /proc directory | Bash Shell | Kernel | Cgroup (98:19)
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Docker inside docker | Dockerd program | Container socket | Sharing base system socket | Chattr command | docker capabilities | Removing capabilities in a container | Adding capabilities in a container | privileged keyword | Cap-drop keyword | Cap-add keyword | Server hardening | Security (124:03)
NLP Training
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Session 1 - 4th Sept-Difference between NLP and nlu | sentiment analysis | implementation of textblob | polarity | subjectivity | objectivity (109:15)
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Session 2 - 5th Sept-Implementation on dataset | data handling with pandas | textblob implementation on data (82:44)
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Session 3 - 6th Sept-Tokenization | lemmatization | stemming | implementation of nltk (103:26)
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Session 4 - 7th Sept-nltk installation | WordNet | Spacy | Automatic Conversion from uppercase to lowercase (113:47)
