Agentic AI Coding

🤝 AI Engineer Agentic Track: The Complete Agent & MCP Course ⭐️ 4.7 - 33778 votes 💳 Original Price: $19.99

📖 Master AI Agents in 30 days: build 8 real-world projects with OpenAI Agents SDK, CrewAI, LangGraph, AutoGen and MCP.

🔊 Taught By: Ed Donner, Ligency ​ 📤 Download Full Course 📤 Download All Courses

🔅 Deep Learning with Python: Foundations 📝 Discover the fundamental concepts and techniques required to implement basic deep learning models using Python. 🌐 Author: Frederick Nwanganga 🔰 Level: Intermediate ⏰ Duration: 1h 54m 📋 Topics: Deep Learning, Python 🔗 Join Artificial intelligence for more courses

🧠 10 Machine Learning Concepts You Must Know ✅ Supervised vs Unsupervised Learning – Understand the foundation of ML tasks ✅ Bias-Variance Tradeoff – Balance underfitting and overfitting ✅ Feature Engineering – The secret sauce to boost model performance ✅ Train-Test Split & Cross-Validation – Evaluate models the right way ✅ Confusion Matrix – Measure model accuracy, precision, recall, and F1 ✅ Gradient Descent – The algorithm behind learning in most models ✅ Regularization (L1/L2) – Prevent overfitting by penalizing complexity ✅ Decision Trees & Random Forests – Interpretable and powerful models ✅ Support Vector Machines – Great for classification with clear boundaries ✅ Neural Networks – The foundation of deep learning

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🔅 Deep Learning with Python: Optimizing Deep Learning Models 📝 Leverage techniques for optimizing deep learning models and implementing them using Python. 🌐 Author: Frederick Nwanganga 🔰 Level: Intermediate ⏰ Duration: 2h 1m 📋 Topics: Deep Learning, Python 🔗 Join Artificial intelligence for more courses

🧠 Most Asked Data Analyst Interview Question ❓ “How do you handle missing data?” ❌ Weak answer: “I remove the rows.” ✅ Strong answer: “It depends on the business impact and data context.” ✔️ Check how much data is missing ✔️ Understand why it’s missing ✔️ Decide based on use case: • Drop rows (if very small % and random) • Impute (mean/median/mode) • Flag missing values • Leave as-is if meaningful 🎯 Interviewer is testing: Your decision-making, not your tools. 💡 Always explain why, not just how. 👍 React if you want Interview Prep #2 tomorrow

1. What is the AdaBoost Algorithm? AdaBoost also called Adaptive Boosting is a technique in Machine Learning used as an Ensemble Method. The most common algorithm used with AdaBoost is decision trees with one level that means with Decision trees with only 1 split. These trees are also called Decision Stumps. What this algorithm does is that it builds a model and gives equal weights to all the data points. It then assigns higher weights to points that are wrongly classified. Now all the points which have higher weights are given more importance in the next model. It will keep training models until and unless a lower error is received. 2. What is the Sliding Window method for Time Series Forecasting? Time series can be phrased as supervised learning. Given a sequence of numbers for a time series dataset, we can restructure the data to look like a supervised learning problem. In the sliding window method, the previous time steps can be used as input variables, and the next time steps can be used as the output variable. In statistics and time series analysis, this is called a lag or lag method. The number of previous time steps is called the window width or size of the lag. This sliding window is the basis for how we can turn any time series dataset into a supervised learning problem. 3. What do you understand by sub-queries in SQL? A subquery is a query inside another query where a query is defined to retrieve data or information back from the database. In a subquery, the outer query is called as the main query whereas the inner query is called subquery. Subqueries are always executed first and the result of the subquery is passed on to the main query. It can be nested inside a SELECT, UPDATE or any other query. A subquery can also use any comparison operators such as >,< or =. 4. Explain the Difference Between Tableau Worksheet, Dashboard, Story, and Workbook? Tableau uses a workbook and sheet file structure, much like Microsoft Excel. A workbook contains sheets, which can be a worksheet, dashboard, or a story. A worksheet contains a single view along with shelves, legends, and the Data pane. A dashboard is a collection of views from multiple worksheets. A story contains a sequence of worksheets or dashboards that work together to convey information. 5. How is a Random Forest related to Decision Trees? Random forest is an ensemble learning method that works by constructing a multitude of decision trees. A random forest can be constructed for both classification and regression tasks. Random forest outperforms decision trees, and it also does not have the habit of overfitting the data as decision trees do. A decision tree trained on a specific dataset will become very deep and cause overfitting. To create a random forest, decision trees can be trained on different subsets of the training dataset, and then the different decision trees can be averaged with the goal of decreasing the variance. 6. What are some disadvantages of using Naive Bayes Algorithm? Some disadvantages of using Naive Bayes Algorithm are: It relies on a very big assumption that the independent variables are not related to each other. It is generally not suitable for datasets with large numbers of numerical attributes. It has been observed that if a rare case is not in the training dataset but is in the testing dataset, then it will most definitely be wrong.

Kandinsky 5.0 Video Lite and Kandinsky 5.0 Video Pro generative models on the global text-to-video landscape 🔘Pro is currently the #1 open-source model worldwide 🔘Lite (2B parameters) outperforms Sora v1. 🔘Only Google (Veo 3.1, Veo 3), OpenAI (Sora 2), Alibaba (Wan 2.5), and KlingAI (Kling 2.5, 2.6) outperform Pro — these are objectively the strongest video generation models in production today. We are on par with Luma AI (Ray 3) and MiniMax (Hailuo 2.3): the maximum ELO gap is 3 points, with a 95% CI of ±21. Useful links 🔘Full leaderboard: LM Arena 🔘Kandinsky 5.0 details: technical report 🔘Open-source Kandinsky 5.0: GitHub and Hugging Face

✅ Power BI Project Ideas for Data Analysts 📊💡 Real-world projects help you stand out in job applications and interviews. 1️⃣ Sales Dashboard • Track revenue, profit, and sales by region/product • Add slicers for year, month, category • Source: Sample Superstore dataset 2️⃣ HR Analytics Dashboard • Analyze employee attrition, performance, and satisfaction • KPIs: attrition rate, avg tenure, engagement score • Use Excel or mock HR dataset 3️⃣ E-commerce Analysis • Show total orders, AOV (average order value), top-selling items • Use date filters, category breakdowns • Optional: add customer segmentation 4️⃣ Financial Report • Monthly expenses vs income • Budget variance tracking • Charts for category-wise breakdown 5️⃣ Healthcare Analytics • Hospital admissions, treatment outcomes, patient demographics • Drill-through: see patient-level detail by department • Public health datasets available online 6️⃣ Marketing Campaign Tracker • Click-through rates, conversion rates, campaign ROI • Compare across channels (email, social, paid ads) 🧠 Bonus Tips: • Use DAX to create measures • Add tooltips and slicers • Make the design clean and professional 📌 Practice Task: Choose one topic → Get a dataset → Build a dashboard → Upload screenshots to GitHub Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c 💬 Tap ❤️ for more!